Merge pull request #80 from cpnota/release/0.2.2

Release/0.2.2

Author: cpnota

all/agents/__init__.py

@@ -1,17 +1,21 @@
-from .abstract import Agent
+from ._agent import Agent
 from .a2c import A2C
-from .actor_critic import ActorCritic
 from .ddpg import DDPG
 from .dqn import DQN
-from .sarsa import Sarsa
+from .ppo import PPO
+from .vac import VAC
 from .vpg import VPG
+from .vqn import VQN
+from .vsarsa import VSarsa
 
 __all__ = [
     "Agent",
     "A2C",
-    "ActorCritic",
     "DDPG",
     "DQN",
-    "Sarsa",
+    "PPO",
+    "VAC",
     "VPG",
+    "VQN",
+    "VSarsa",
 ]

all/agents/abstract.py renamed to all/agents/_agent.py

@@ -1,7 +1,7 @@
 import torch
 from all.environments import State
-from all.memory import NStepBatchBuffer
-from .abstract import Agent
+from all.memory import NStepAdvantageBuffer
+from ._agent import Agent
 
 
 class A2C(Agent):
@@ -36,20 +36,17 @@ def act(self, states, rewards):
     def _train(self):
         if len(self._buffer) >= self._batch_size:
             states = State.from_list(self._features)
-            _, _, returns, next_states, rollout_lengths = self._buffer.sample(self._batch_size)
-            td_errors = (
-                returns
-                + (self.discount_factor ** rollout_lengths)
-                * self.v.eval(self.features.eval(next_states))
-                - self.v(states)
-            )
-            self.v.reinforce(td_errors)
-            self.policy.reinforce(td_errors)
+            _, _, advantages = self._buffer.sample(self._batch_size)
+            self.v(states)
+            self.v.reinforce(advantages)
+            self.policy.reinforce(advantages)
             self.features.reinforce()
             self._features = []
 
     def _make_buffer(self):
-        return NStepBatchBuffer(
+        return NStepAdvantageBuffer(
+            self.v,
+            self.features,
             self.n_steps,
             self.n_envs,
             discount_factor=self.discount_factor

all/agents/a2c.py

@@ -1,5 +1,5 @@
 import torch
-from .abstract import Agent
+from ._agent import Agent
 
 class DDPG(Agent):
     def __init__(self,

all/agents/actor_critic.py

@@ -1,5 +1,5 @@
 import torch
-from .abstract import Agent
+from ._agent import Agent
 
 
 class DQN(Agent):

all/agents/ddpg.py

@@ -0,0 +1,92 @@
+import torch
+from all.memory import GeneralizedAdvantageBuffer
+from ._agent import Agent
+
+
+class PPO(Agent):
+    def __init__(
+            self,
+            features,
+            v,
+            policy,
+            epsilon=0.2,
+            epochs=4,
+            minibatches=4,
+            n_envs=None,
+            n_steps=4,
+            discount_factor=0.99,
+            lam=0.95
+    ):
+        if n_envs is None:
+            raise RuntimeError("Must specify n_envs.")
+        self.features = features
+        self.v = v
+        self.policy = policy
+        self.n_envs = n_envs
+        self.n_steps = n_steps
+        self.discount_factor = discount_factor
+        self.lam = lam
+        self._epsilon = epsilon
+        self._epochs = epochs
+        self._batch_size = n_envs * n_steps
+        self._minibatches = minibatches
+        self._buffer = self._make_buffer()
+        self._features = []
+
+    def act(self, states, rewards):
+        self._train()
+        actions = self.policy.eval(self.features.eval(states))
+        self._buffer.store(states, actions, rewards)
+        return actions
+
+    def _train(self):
+        if len(self._buffer) >= self._batch_size:
+            states, actions, advantages = self._buffer.sample(self._batch_size)
+            with torch.no_grad():
+                features = self.features.eval(states)
+                pi_0 = self.policy.eval(features, actions)
+                targets = self.v.eval(features) + advantages
+            for _ in range(self._epochs):
+                self._train_epoch(states, actions, pi_0, advantages, targets)
+
+    def _train_epoch(self, states, actions, pi_0, advantages, targets):
+        minibatch_size = int(self._batch_size / self._minibatches)
+        indexes = torch.randperm(self._batch_size)
+        for n in range(self._minibatches):
+            first = n * minibatch_size
+            last = first + minibatch_size
+            i = indexes[first:last]
+            self._train_minibatch(states[i], actions[i], pi_0[i], advantages[i], targets[i])
+
+    def _train_minibatch(self, states, actions, pi_0, advantages, targets):
+        features = self.features(states)
+        self.policy(features, actions)
+        self.policy.reinforce(self._compute_policy_loss(pi_0, advantages))
+        self.v.reinforce(targets - self.v(features))
+        self.features.reinforce()
+
+    def _compute_targets(self, returns, next_states, lengths):
+        return (
+            returns +
+            (self.discount_factor ** lengths)
+            * self.v.eval(self.features.eval(next_states))
+        )
+
+    def _compute_policy_loss(self, pi_0, advantages):
+        def _policy_loss(pi_i):
+            ratios = torch.exp(pi_i - pi_0)
+            surr1 = ratios * advantages
+            surr2 = torch.clamp(ratios, 1.0 - self._epsilon, 1.0 + self._epsilon) * advantages
+            return -torch.min(surr1, surr2).mean()
+        return _policy_loss
+
+    def _make_buffer(self):
+        return GeneralizedAdvantageBuffer(
+            self.v,
+            self.features,
+            self.n_steps,
+            self.n_envs,
+            discount_factor=self.discount_factor,
+            lam=self.lam
+        )
+ 

all/agents/dqn.py

@@ -0,0 +1,23 @@
+from ._agent import Agent
+
+class VAC(Agent):
+    '''Vanilla Actor-Critic'''
+    def __init__(self, features, v, policy, gamma=1):
+        self.features = features
+        self.v = v
+        self.policy = policy
+        self.gamma = gamma
+        self._previous_features = None
+
+    def act(self, state, reward):
+        if self._previous_features:
+            td_error = (
+                reward
+                + self.gamma * self.v.eval(self.features.eval(state))
+                - self.v(self._previous_features)
+            )
+            self.v.reinforce(td_error)
+            self.policy.reinforce(td_error)
+            self.features.reinforce()
+        self._previous_features = self.features(state)
+        return self.policy(self._previous_features)

all/agents/ppo.py

@@ -1,21 +1,25 @@
 import torch
 from all.environments import State
-from .abstract import Agent
+from ._agent import Agent
 
 class VPG(Agent):
+    '''Vanilla Policy Gradient'''
     def __init__(
             self,
             features,
             v,
             policy,
             gamma=0.99,
-            n_episodes=1
+            # run complete episodes until we have
+            # seen at least min_batch_size states
+            min_batch_size=1
     ):
         self.features = features
         self.v = v
         self.policy = policy
         self.gamma = gamma
-        self.n_episodes = n_episodes
+        self.min_batch_size = min_batch_size
+        self._current_batch_size = 0
         self._trajectories = []
         self._features = []
         self._rewards = []
@@ -43,10 +47,11 @@ def _terminal(self, reward):
         features = torch.cat(self._features)
         rewards = torch.tensor(self._rewards, device=features.device)
         self._trajectories.append((features, rewards))
+        self._current_batch_size += len(features)
         self._features = []
         self._rewards = []
 
-        if len(self._trajectories) >= self.n_episodes:
+        if self._current_batch_size >= self.min_batch_size:
             self._train()
 
     def _train(self):
@@ -59,6 +64,7 @@ def _train(self):
         self.policy.reinforce(advantages)
         self.features.reinforce()
         self._trajectories = []
+        self._current_batch_size = 0
 
     def _compute_advantages(self, features, rewards):
         returns = self._compute_discounted_returns(rewards)

all/agents/vac.py

@@ -0,0 +1,26 @@
+import torch
+from ._agent import Agent
+
+
+class VQN(Agent):
+    '''Vanilla Q-Network'''
+    def __init__(self, q, policy, gamma=1):
+        self.q = q
+        self.policy = policy
+        self.gamma = gamma
+        self.env = None
+        self.previous_state = None
+        self.previous_action = None
+
+    def act(self, state, reward):
+        action = self.policy(state)
+        if self.previous_state:
+            td_error = (
+                reward
+                + self.gamma * torch.max(self.q.eval(state), dim=1)[0]
+                - self.q(self.previous_state, self.previous_action)
+            )
+            self.q.reinforce(td_error)
+        self.previous_state = state
+        self.previous_action = action
+        return action