Addressing issue 145 (#160)

* Addressing issue 145

* Fixed setup script env_params to reflect new changes

* Fixed pep8 issues

* Added new green_wave_env requirements to rllib/green_wave.py

* Added new env params to the benchmark experiments as well

* Added env params to baseline as well. nose2 not passing for some figure_eight scenarios

Author: kjang96

examples/rllab/green_wave.py

@@ -106,8 +106,10 @@ def run_task(*_):
 
     additional_env_params = {
         "target_velocity": 50,
-        "num_steps": 500,
-        "switch_time": 3.0
+        "switch_time": 3.0,
+        "num_observed": 2,
+        "discrete": False,
+        "tl_type": "controlled"
     }
     env_params = EnvParams(additional_params=additional_env_params)
 

examples/rllib/green_wave.py

@@ -95,7 +95,13 @@ def get_non_flow_params(enter_speed, additional_net_params):
     "rl_veh": rl_veh
 }
 
-additional_env_params = {"target_velocity": 50, "switch_time": 3.0}
+additional_env_params = {
+        "target_velocity": 50,
+        "switch_time": 3.0,
+        "num_observed": 2,
+        "discrete": False,
+        "tl_type": "controlled"
+    }
 
 additional_net_params = {
     "speed_limit": 35,

flow/benchmarks/baselines/grid0.py

@@ -123,9 +123,11 @@ class needed to run simulations
             evaluate=True,  # Set to True to evaluate traffic metrics
             horizon=HORIZON,
             additional_params={
-                "switch_time": 2.0,
+                "target_velocity": 50,
+                "switch_time": 2,
                 "num_observed": 2,
-                "tl_type": "actuated",
+                "discrete": False,
+                "tl_type": "controlled"
             },
         )
 

flow/benchmarks/baselines/grid1.py

@@ -123,9 +123,11 @@ class needed to run simulations
             evaluate=True,  # Set to True to evaluate traffic metrics
             horizon=HORIZON,
             additional_params={
-                "switch_time": 2.0,
+                "target_velocity": 50,
+                "switch_time": 2,
                 "num_observed": 2,
-                "tl_type": "actuated",
+                "discrete": False,
+                "tl_type": "controlled"
             },
         )
 

flow/benchmarks/grid0.py

@@ -87,8 +87,11 @@
     env=EnvParams(
         horizon=HORIZON,
         additional_params={
-            "switch_time": 2.0,
+            "target_velocity": 50,
+            "switch_time": 2,
             "num_observed": 2,
+            "discrete": False,
+            "tl_type": "controlled"
         },
     ),
 

flow/benchmarks/grid1.py

@@ -87,8 +87,11 @@
     env=EnvParams(
         horizon=HORIZON,
         additional_params={
-            "switch_time": 2.0,
+            "target_velocity": 50,
+            "switch_time": 2,
             "num_observed": 2,
+            "discrete": False,
+            "tl_type": "controlled"
         },
     ),
 

flow/envs/green_wave_env.py

@@ -18,6 +18,13 @@
     "discrete": False,
 }
 
+ADDITIONAL_PO_ENV_PARAMS = {
+    # num of vehicles the agent can observe on each incoming edge
+    "num_observed": 2,
+    # velocity to use in reward functions
+    "target_velocity": 30,
+}
+
 
 class TrafficLightGridEnv(Env):
     """Environment used to train traffic lights to regulate traffic flow
@@ -57,6 +64,12 @@ class TrafficLightGridEnv(Env):
     """
 
     def __init__(self, env_params, sumo_params, scenario):
+
+        for p in ADDITIONAL_ENV_PARAMS.keys():
+            if p not in env_params.additional_params:
+                raise KeyError(
+                    'Environment parameter "{}" not supplied'.format(p))
+
         self.grid_array = scenario.net_params.additional_params["grid_array"]
         self.rows = self.grid_array["row_num"]
         self.cols = self.grid_array["col_num"]
@@ -458,15 +471,15 @@ class PO_TrafficLightGridEnv(TrafficLightGridEnv):
     def __init__(self, env_params, sumo_params, scenario):
         super().__init__(env_params, sumo_params, scenario)
 
+        for p in ADDITIONAL_PO_ENV_PARAMS.keys():
+            if p not in env_params.additional_params:
+                raise KeyError(
+                    'Environment parameter "{}" not supplied'.format(p))
+
         # number of vehicles nearest each intersection that is observed in the
         # state space; defaults to 2
         self.num_observed = env_params.additional_params.get("num_observed", 2)
 
-        # used while computing the reward
-        self.env_params.additional_params["target_velocity"] = \
-            max(self.scenario.speed_limit(edge)
-                for edge in self.scenario.get_edge_list())
-
         # used during visualization
         self.observed_ids = []
 

tests/setup_scripts.py

@@ -358,8 +358,9 @@ def grid_mxn_exp_setup(row_num=1,
         # set default env_params configuration
         additional_env_params = {
             "target_velocity": 50,
-            "num_steps": 100,
-            "switch_time": 3.0
+            "switch_time": 3.0,
+            "tl_type": "controlled",
+            "discrete": False
         }
 
         env_params = EnvParams(