julia lsp too slow to use in helix

[haxfn]

output of hx -v --log log.txt:

2025-06-22T18:58:43.316 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"initialize","params":{"capabilities":{"general":{"positionEncodings":["utf-8","utf-32","utf-16"]},"textDocument":{"codeAction":{"codeActionLiteralSupport":{"codeActionKind":{"valueSet":["","quickfix","refactor","refactor.extract","refactor.inline","refactor.rewrite","source","source.organizeImports"]}},"dataSupport":true,"disabledSupport":true,"isPreferredSupport":true,"resolveSupport":{"properties":["edit","command"]}},"completion":{"completionItem":{"deprecatedSupport":true,"insertReplaceSupport":true,"resolveSupport":{"properties":["documentation","detail","additionalTextEdits"]},"snippetSupport":true,"tagSupport":{"valueSet":[1]}},"completionItemKind":{}},"formatting":{"dynamicRegistration":false},"hover":{"contentFormat":["markdown"]},"inlayHint":{"dynamicRegistration":false},"publishDiagnostics":{"tagSupport":{"valueSet":[1,2]},"versionSupport":true},"rename":{"dynamicRegistration":false,"honorsChangeAnnotations":false,"prepareSupport":true},"signatureHelp":{"signatureInformation":{"activeParameterSupport":true,"documentationFormat":["markdown"],"parameterInformation":{"labelOffsetSupport":true}}}},"window":{"workDoneProgress":true},"workspace":{"applyEdit":true,"configuration":true,"didChangeConfiguration":{"dynamicRegistration":false},"didChangeWatchedFiles":{"dynamicRegistration":true,"relativePatternSupport":false},"executeCommand":{"dynamicRegistration":false},"fileOperations":{"didRename":true,"willRename":true},"inlayHint":{"refreshSupport":false},"symbol":{"dynamicRegistration":false},"workspaceEdit":{"documentChanges":true,"failureHandling":"abort","normalizesLineEndings":false,"resourceOperations":["create","rename","delete"]},"workspaceFolders":true}},"clientInfo":{"name":"helix","version":"25.01.1 (171dfc60)"},"processId":61198,"rootPath":"/home/vishal/Documents/Repos/RLExperiments","rootUri":"file:///home/vishal/Documents/Repos/RLExperiments","workspaceFolders":[{"name":"RLExperiments","uri":"file:///home/vishal/Documents/Repos/RLExperiments"}]},"id":0}
2025-06-22T18:59:01.481 helix_lsp::transport [INFO] julia <- {"id":0,"jsonrpc":"2.0","result":{"capabilities":{"textDocumentSync":{"openClose":true,"change":1,"willSave":false,"willSaveWaitUntil":false,"save":{"includeText":true}},"completionProvider":{"resolveProvider":false,"triggerCharacters":[".","@","\"","^"]},"hoverProvider":true,"signatureHelpProvider":{"triggerCharacters":["(",","]},"declarationProvider":false,"definitionProvider":true,"typeDefinitionProvider":false,"implementationProvider":false,"referencesProvider":true,"documentHighlightProvider":true,"documentSymbolProvider":true,"codeActionProvider":true,"colorProvider":false,"documentFormattingProvider":true,"documentRangeFormattingProvider":false,"renameProvider":{"prepareProvider":true},"foldingRangeProvider":false,"executeCommandProvider":{"commands":["FixMissingRef","ReexportModule","ExpandFunction","ExplicitPackageVarImport"]},"selectionRangeProvider":true,"workspaceSymbolProvider":true,"workspace":{"workspaceFolders":{"supported":true,"changeNotifications":true}}}}}
2025-06-22T18:59:01.481 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"initialized","params":{}}
2025-06-22T18:59:01.481 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didOpen","params":{"textDocument":{"languageId":"julia","text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\n\\pi\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n","uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":3}}}
2025-06-22T18:59:04.637 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/notebooks/Easy21.jl","version":0,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:04.814 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/notebooks/MCControl.jl","version":0,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:04.814 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/notebooks/MCPrediction.jl","version":0,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:04.814 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/Control.jl","version":0,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:04.814 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/Easy21.jl","version":0,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:04.814 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":0,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:04.814 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/RLExperiments.jl","version":0,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:04.814 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/RLExperimentsUtils.jl","version":0,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:04.814 helix_view::handlers::lsp [INFO] Version (0) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (3)), dropping PublishDiagnostic notification
2025-06-22T18:59:04.814 helix_lsp::transport [INFO] julia <- {"method":"workspace/configuration","id":"865febf3-6729-4dd7-bee6-5038a08a4c72","params":{"items":[{"section":"julia.format.indent"},{"section":"julia.format.indents"},{"section":"julia.format.ops"},{"section":"julia.format.tuples"},{"section":"julia.format.curly"},{"section":"julia.format.calls"},{"section":"julia.format.iterOps"},{"section":"julia.format.comments"},{"section":"julia.format.docs"},{"section":"julia.format.lineends"},{"section":"julia.format.keywords"},{"section":"julia.format.kwarg"},{"section":"julia.lint.call"},{"section":"julia.lint.iter"},{"section":"julia.lint.nothingcomp"},{"section":"julia.lint.constif"},{"section":"julia.lint.lazy"},{"section":"julia.lint.datadecl"},{"section":"julia.lint.typeparam"},{"section":"julia.lint.modname"},{"section":"julia.lint.pirates"},{"section":"julia.lint.useoffuncargs"},{"section":"julia.lint.run"},{"section":"julia.lint.missingrefs"},{"section":"julia.lint.disabledDirs"},{"section":"julia.completionmode"}]},"jsonrpc":"2.0"}
2025-06-22T18:59:04.814 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","result":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"id":"865febf3-6729-4dd7-bee6-5038a08a4c72"}
2025-06-22T18:59:05.439 helix_lsp::transport [INFO] julia <- {"method":"window/workDoneProgress/create","id":"069a4101-1d26-4c49-a992-6b67fa108b9d","params":{"token":"4bff701e-72a0-4af0-8b53-aba19895340e"},"jsonrpc":"2.0"}
2025-06-22T18:59:05.439 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","result":null,"id":"069a4101-1d26-4c49-a992-6b67fa108b9d"}
2025-06-22T18:59:06.108 helix_lsp::transport [INFO] julia <- {"method":"$/progress","params":{"token":"4bff701e-72a0-4af0-8b53-aba19895340e","value":{"kind":"begin","title":"Julia Language Server","message":"Indexing packages..."}},"jsonrpc":"2.0"}
2025-06-22T18:59:06.108 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":3,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:08.065 helix_lsp::transport [ERROR] julia err <- "ERROR: MethodError: no method matching (::LanguageServer.var\"#93#95\"{LanguageServerInstance})(::String, ::Int64)\n"
2025-06-22T18:59:08.187 helix_lsp::transport [ERROR] julia err <- "The function `#93` exists, but no method is defined for this combination of argument types.\n"
2025-06-22T18:59:08.187 helix_lsp::transport [ERROR] julia err <- "\n"
2025-06-22T18:59:08.187 helix_lsp::transport [ERROR] julia err <- "Closest candidates are:\n"
2025-06-22T18:59:08.187 helix_lsp::transport [ERROR] julia err <- "  (::LanguageServer.var\"#93#95\")(::Any)\n"
2025-06-22T18:59:08.187 helix_lsp::transport [ERROR] julia err <- "   @ LanguageServer ~/.julia/packages/LanguageServer/JrIEf/src/languageserverinstance.jl:185\n"
2025-06-22T18:59:08.199 helix_lsp::transport [ERROR] julia err <- "\n"
2025-06-22T18:59:08.199 helix_lsp::transport [ERROR] julia err <- "Stacktrace:\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- " [1] (::SymbolServer.var\"#70#72\"{LanguageServer.var\"#93#95\"{LanguageServerInstance}})(msg::String, p::Int64)\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- "   @ SymbolServer ~/.julia/packages/SymbolServer/2jiVV/src/SymbolServer.jl:151\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- " [2] (::SymbolServer.var\"#65#67\"{SymbolServer.SymbolServerInstance, String, SymbolServer.var\"#70#72\"{LanguageServer.var\"#93#95\"{LanguageServerInstance}}})(download_dir::String)\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- "   @ SymbolServer ~/.julia/packages/SymbolServer/2jiVV/src/SymbolServer.jl:126\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- " [3] mktempdir(fn::SymbolServer.var\"#65#67\"{SymbolServer.SymbolServerInstance, String, SymbolServer.var\"#70#72\"{LanguageServer.var\"#93#95\"{LanguageServerInstance}}}, parent::String; prefix::String)\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- "   @ Base.Filesystem ./file.jl:819\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- " [4] mktempdir\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- "   @ ./file.jl:815 [inlined]\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- " [5] download_cache_files\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- "   @ ~/.julia/packages/SymbolServer/2jiVV/src/SymbolServer.jl:99 [inlined]\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- " [6] getstore(ssi::SymbolServer.SymbolServerInstance, environment_path::String, progress_callback::LanguageServer.var\"#93#95\"{LanguageServerInstance}, error_handler::Nothing; download::Bool)\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- "   @ SymbolServer ~/.julia/packages/SymbolServer/2jiVV/src/SymbolServer.jl:156\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- " [7] getstore\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- "   @ ~/.julia/packages/SymbolServer/2jiVV/src/SymbolServer.jl:149 [inlined]\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- " [8] (::LanguageServer.var\"#92#94\"{LanguageServerInstance})()\n"
2025-06-22T18:59:08.216 helix_lsp::transport [ERROR] julia err <- "   @ LanguageServer ~/.julia/packages/LanguageServer/JrIEf/src/languageserverinstance.jl:182\n"
2025-06-22T18:59:13.790 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\n\\p\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":4}}}
2025-06-22T18:59:13.803 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:13.912 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/signatureHelp","params":{"position":{"character":2,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":1}
2025-06-22T18:59:13.958 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\n\\\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":5}}}
2025-06-22T18:59:13.971 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:14.080 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/signatureHelp","params":{"position":{"character":1,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":2}
2025-06-22T18:59:14.113 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\n\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":6}}}
2025-06-22T18:59:14.127 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:14.235 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/signatureHelp","params":{"position":{"character":0,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":3}
2025-06-22T18:59:14.436 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nm\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":7}}}
2025-06-22T18:59:14.449 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:14.557 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/signatureHelp","params":{"position":{"character":1,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":4}
2025-06-22T18:59:14.580 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nmo\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":8}}}
2025-06-22T18:59:14.653 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":4,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:14.653 helix_view::handlers::lsp [INFO] Version (4) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (8)), dropping PublishDiagnostic notification
2025-06-22T18:59:14.700 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/signatureHelp","params":{"position":{"character":2,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":5}
2025-06-22T18:59:14.778 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nmod\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":9}}}
2025-06-22T18:59:14.899 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/signatureHelp","params":{"position":{"character":3,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":6}
2025-06-22T18:59:14.912 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nmodu\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":10}}}
2025-06-22T18:59:14.925 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:15.003 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nmodul\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":11}}}
2025-06-22T18:59:15.124 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/signatureHelp","params":{"position":{"character":5,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":7}
2025-06-22T18:59:15.250 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nmodule\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":12}}}
2025-06-22T18:59:15.263 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:15.371 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/signatureHelp","params":{"position":{"character":6,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":8}
2025-06-22T18:59:15.490 helix_lsp::transport [INFO] julia <- {"id":1,"jsonrpc":"2.0","result":{"signatures":[],"activeSignature":0,"activeParameter":0}}
2025-06-22T18:59:15.490 helix_lsp::transport [ERROR] Tried sending response into a closed channel (id=Num(1)), original request likely timed out
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":5,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"id":2,"jsonrpc":"2.0","result":{"signatures":[],"activeSignature":0,"activeParameter":0}}
2025-06-22T18:59:15.498 helix_lsp::transport [ERROR] Tried sending response into a closed channel (id=Num(2)), original request likely timed out
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":6,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"id":3,"jsonrpc":"2.0","result":{"signatures":[],"activeSignature":0,"activeParameter":0}}
2025-06-22T18:59:15.498 helix_lsp::transport [ERROR] Tried sending response into a closed channel (id=Num(3)), original request likely timed out
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":7,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"id":4,"jsonrpc":"2.0","result":{"signatures":[],"activeSignature":0,"activeParameter":0}}
2025-06-22T18:59:15.498 helix_lsp::transport [ERROR] Tried sending response into a closed channel (id=Num(4)), original request likely timed out
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":8,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"id":5,"jsonrpc":"2.0","result":{"signatures":[],"activeSignature":0,"activeParameter":0}}
2025-06-22T18:59:15.498 helix_lsp::transport [ERROR] Tried sending response into a closed channel (id=Num(5)), original request likely timed out
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":9,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"id":6,"jsonrpc":"2.0","result":{"signatures":[],"activeSignature":0,"activeParameter":0}}
2025-06-22T18:59:15.498 helix_view::handlers::lsp [INFO] Version (5) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (12)), dropping PublishDiagnostic notification
2025-06-22T18:59:15.498 helix_lsp::transport [ERROR] Tried sending response into a closed channel (id=Num(6)), original request likely timed out
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":10,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:15.498 helix_view::handlers::lsp [INFO] Version (6) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (12)), dropping PublishDiagnostic notification
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":11,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:15.498 helix_view::handlers::lsp [INFO] Version (7) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (12)), dropping PublishDiagnostic notification
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"id":7,"jsonrpc":"2.0","result":{"signatures":[],"activeSignature":0,"activeParameter":0}}
2025-06-22T18:59:15.498 helix_lsp::transport [ERROR] Tried sending response into a closed channel (id=Num(7)), original request likely timed out
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":12,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:15.498 helix_view::handlers::lsp [INFO] Version (8) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (12)), dropping PublishDiagnostic notification
2025-06-22T18:59:15.498 helix_lsp::transport [INFO] julia <- {"id":8,"jsonrpc":"2.0","result":{"signatures":[],"activeSignature":0,"activeParameter":0}}
2025-06-22T18:59:15.498 helix_view::handlers::lsp [INFO] Version (9) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (12)), dropping PublishDiagnostic notification
2025-06-22T18:59:15.498 helix_view::handlers::lsp [INFO] Version (10) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (12)), dropping PublishDiagnostic notification
2025-06-22T18:59:15.498 helix_view::handlers::lsp [INFO] Version (11) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (12)), dropping PublishDiagnostic notification
2025-06-22T18:59:15.501 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/completion","params":{"context":{"triggerKind":1},"position":{"character":6,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":9}
2025-06-22T18:59:19.960 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nmodul\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":13}}}
2025-06-22T18:59:19.974 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:20.010 helix_lsp::transport [INFO] julia <- {"id":9,"jsonrpc":"2.0","result":{"isIncomplete":true,"items":[{"label":"module","kind":14,"documentation":"module","insertTextFormat":2,"textEdit":{"range":{"start":{"line":3,"character":0},"end":{"line":3,"character":6}},"newText":"module $1\n\t$0\nend"}}]}}
2025-06-22T18:59:20.012 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":13,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:22.843 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nmodule\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":17}}}
2025-06-22T18:59:22.845 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nmodule \n\t\nend\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":18}}}
2025-06-22T18:59:22.847 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":17,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:22.849 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":18,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:22.855 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:22.857 helix_view::handlers::lsp [INFO] Version (17) is out of date for File("/home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl") (expected (18)), dropping PublishDiagnostic notification
2025-06-22T18:59:24.515 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":19}}}
2025-06-22T18:59:24.519 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":19,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:24.528 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:27.046 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didChange","params":{"contentChanges":[{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\n\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n"}],"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":20}}}
2025-06-22T18:59:27.049 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":20,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:27.059 helix_vcs::diff::worker [INFO] Diff computation timed out, update of diffs might appear delayed
2025-06-22T18:59:27.167 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/signatureHelp","params":{"position":{"character":0,"line":3},"textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}},"id":10}
2025-06-22T18:59:27.167 helix_lsp::transport [INFO] julia <- {"id":10,"jsonrpc":"2.0","result":{"signatures":[],"activeSignature":0,"activeParameter":0}}
2025-06-22T18:59:29.428 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"textDocument/didSave","params":{"text":"# todo: write modular components, and make places for those boxes to be fitted:\n# gpi[pe,pi], pe[visit,on/ffline], pi[greedy/egreedy]\nmodule MCPrediction\n\nimport Distributions\nimport GLMakie\n\ninclude(\"./RLExperimentsUtils.jl\")\ninclude(\"./Easy21.jl\")\n\nimport .RLExperimentsUtils:@do_while,sample,isconverged\nimport .Easy21\n\nPolicy = Matrix{Float64}\nStateValue = Vector{Float64}\nActionValue = Matrix{Float64}\n\nfunction createrandompolicy()\n\t# π = rand(length(Easy21.states), length(Easy21.actions))\n\t# π ./= sum(π, dims=2)\n\tπ = Matrix((rand(Distributions.Dirichlet(length(Easy21.actions), 1), length(Easy21.states)))')\n\t# samples from triangle rather than cube -> triangle projection\n\t# # verify random policy\n\t# begin\n\t# \tπ = createrandompolicy()\n\t# \tisvalidpolicy = all(isapprox.(sum(π, dims=2), 1.0))\n\t# \tstring(\"ispolicyvalid ?= \", isvalidpolicy)\n\t# end\n\tπ\nend\n\nfunction createepisode(s₁::Easy21.StartState, π::Policy)::Easy21.Episode\n\tepisode = []\n\ts = s₁\n\tr = 0\n\twhile r == 0\n\t\tsi = Easy21.stateindex(s)\n\t\tπ_s = π[si, :]\n\t\ta = sample(Easy21.actions, π_s)\n\t\t(s′, r) = Easy21.step(s, a)\n\t\tpush!(episode, (s, a, r))\n\t\ts = s′\n\tend\n\t\n\tepisode\nend\n\nfunction mcpolicyevaluation(q₁::ActionValue, π::Policy; k=UInt64)::ActionValue\n\tγ = 1.0  # should I include this inside Env?\n\n\tq̂_π = q₁\n\tq_π = zeros((size(q₁))...)\n\n\tg = zeros((size(q₁))...)\n\tn = zeros((size(q₁))...)\n\n\tnum_iter = 0\n\t@do_while begin\n\t\tnum_iter += 1\n\t\tq_π = q̂_π\n\t\t\n\t\ts = Easy21.initialstate()\n\t\tepisode = createepisode(s, π)\n\t\t\n\t\tgₜ = 0\n\t\tfor t in length(episode):-1:1\n\t\t\tsₜ, aₜ, r = episode[t]\n\t\t\tgₜ = + r + γ * gₜ\n\t\t\t(si, ai) = (Easy21.stateindex(sₜ), Easy21.actionindex(aₜ))\n\t\t\tn[si, ai] += 1\n\t\t\tg[si, ai] += gₜ\n\t\tend\n\t\tq̂_π = g ./ n\n\n\t\tisactionvaluenotconverged = !(isconverged(q_π, q̂_π))\n\t\t(isactionvaluenotconverged) ? nothing : println(\"ActionValue Converged! num_iter = \", num_iter)\n\tend ((num_iter < k) && (isactionvaluenotconverged))\n\n\tq_π\nend\n\nfunction main()\n\ts₁ = Easy21.initialstate()\n\tπ₁ = createrandompolicy()\n\tepisode = createepisode(s₁, π₁)\n\n\tq₁ = zeros(length(Easy21.states), length(Easy21.actions))\n\tq_π₁ = mcpolicyevaluation(q₁, π₁; k=2000000)\n\tv_π₁ = vec(maximum(q_π₁, dims=2))\n\n\tplot(v_π₁)\nend\n\n# PLOTTING-STATEVALUE === {{{\nfunction plot(v_π₁::StateValue)\n\txs = zeros(length(Easy21.states))\n\tys = zeros(length(Easy21.states))\n\tzs = zeros(length(Easy21.states))\n\t\n\tfor si in 1:length(Easy21.states)\n\t\ts = Easy21.states[si]\n\t\t(y, x) = (dealerfirstcard, playersum) = s\n\t\tz = v_π₁[si]\n\t\n\t\txs[si] = x\n\t\tys[si] = y\n\t\tzs[si] = z\n\tend\n\n\tfig = GLMakie.Figure()\n\tax3 = GLMakie.Axis3(\n\t\tfig[1, 1],\n\t\txlabel = \"Dealer Showing\", \n\t\tylabel = \"Player Sum\", \n\t\tzlabel = \"v_π₁(s)\",\n\t\ttitle = \"mcpolicyevaluaiton\"\n\t)\n\t# GLMakie.limits!(ax3, (1, 21), (1, 10), (-1, 1))\n\tGLMakie.surface!(ax3, xs, ys, zs)\n\n\tfig\nend\n# }}} === PLOTTING-STATEVALUE\n\n\n# PRETTY-PRINTING === {{{\n\nfunction Base.show(io::IO, ::MIME\"text/plain\", actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n\nfunction Base.show(io::IO, actionvalue::ActionValue)\n\tprint(io, \"[\\n\")\n\tfor i in 1:size(actionvalue)[1]\n\t    print(io, i, ' ', actionvalue[i, :], '\\n')  # todo, format: right-align the state number column\n    end\n\tprint(io, \"]\\n\")\nend\n# }}} === PRETTY-PRINTING\n\nend # module MCPrediction\n","textDocument":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl"}}}
2025-06-22T18:59:29.429 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"shutdown","id":11}
2025-06-22T18:59:29.585 helix_lsp::transport [INFO] julia <- {"method":"textDocument/publishDiagnostics","params":{"uri":"file:///home/vishal/Documents/Repos/RLExperiments/src/MCPrediction.jl","version":20,"diagnostics":[]},"jsonrpc":"2.0"}
2025-06-22T18:59:29.585 helix_lsp::transport [INFO] julia <- {"id":11,"jsonrpc":"2.0","result":null}
2025-06-22T18:59:29.585 helix_lsp::transport [INFO] julia -> {"jsonrpc":"2.0","method":"exit"}

[haxfn]

Other information:

julia version 1.11.5
LanguageServer v4.1.0
helix 25.01.1 (171dfc6)

Linux turing 6.15.2-arch1-1 #1 SMP PREEMPT_DYNAMIC Tue, 10 Jun 2025 21:32:33 +0000 x86_64 GNU/Linux

using no config in languages.toml for julia

Configured language servers:
✓ julia: /home/vishal/.juliaup/bin/julia
Configured debug adapter: None
Configured formatter: None
Tree-sitter parser: ✓
Highlight queries: ✓
Textobject queries: ✓
Indent queries: ✓

[Guillawme]

Hello,

Do you have many packages installed in the default Julia environment? This often slows things down significantly, as inter-dependencies between these packages cause a lot of recompilation.

Try this:

1. Create a shared Julia environment named @lsp (or any name you want, but remember its name, and it has to start with @ to be a shared environment).
2. Activate this environment and install the LanguageServer package in it. Don't install anything else in this environment, or a few development tools if you want, but try to keep the installed packages to a minimum. Julia has "stacked" environments, which means you can have utility packages in dedicated shared environments and packages your projects depend on in the corresponding project environments, and still "see" all of them when you work in the project environment (more explanations here). So, for something like LSP that will run as a background process, it makes sense to create a minimal environment.
3. Add the following entry in the languages.toml file in your helix configuration:

[language-server]
julia = { command = "julia", timeout = 60, args = [ "--project=@lsp", "--startup-file=no", "--history-file=no", "--quiet", "-e", "using LanguageServer; runserver()" ] }

The larger than default timeout = 60 will allow helix to wait for Julia to finish its operations, --project=@lsp will start the Julia process in this environment with few packages (make sure the name matches the one you gave to the shared environment in step 1), --startup-file=no will speed up the startup by skipping any custom configuration you have for Julia (which is typically useful when using the REPL interactively, but not for a background process).

I have not tried using LSP with this setup, but I hope this helps!

[haxfn]

I'll try

[Guillawme]

Please let me know if it helps.

[BenitoEck]

Julia LanguageServer.jl unfortunately is not the fastest, especially on the first run. Working with environments however is the recommended way anyway and can limit the number of packages the LSP has to cache. Open the REPL in your project directory, enter package mode via ], then activate . and add YourDependency.jl. To make the script transferable you can automate the whole package loading and the instantiation process like in this example:

# Bootstrapping our environment from Project.toml and Manifest.toml
using Pkg
Pkg.activate(@__DIR__)
Pkg.instantiate()

using CSV, DataFrames, StringEncodings, Dates, Logging, XLSX, StatsBase