Merge branch 'master' of https://github.com/Brandon-Rozek/rltorch

# Conflicts:
#	rltorch/agents/QEPAgent.py

rltorch/agents/DQNAgent.py

@@ -4,6 +4,7 @@ import torch
 import torch.nn.functional as F
 from copy import deepcopy
 import numpy as np
+from pathlib import Path
 
 class DQNAgent:
     def __init__(self, net , memory, config, target_net = None, logger = None):
@@ -12,6 +13,12 @@ class DQNAgent:
         self.memory = memory
         self.config = deepcopy(config)
         self.logger = logger
+    def save(self, file_location):
+        torch.save(self.net.model.state_dict(), file_location)
+    def load(self, file_location):
+        self.net.model.state_dict(torch.load(file_location))
+        self.net.model.to(self.net.device)
+        self.target_net.sync()
 
     def learn(self, logger = None):
         if len(self.memory) < self.config['batch_size']:
@@ -57,8 +64,10 @@ class DQNAgent:
 
         # If we're sampling by TD error, multiply loss by a importance weight which helps decrease overfitting
         if (isinstance(self.memory, M.PrioritizedReplayMemory)):
+            # loss = (torch.as_tensor(importance_weights, device = self.net.device) * F.smooth_l1_loss(obtained_values, expected_values, reduction = 'none').squeeze(1)).mean()
              loss = (torch.as_tensor(importance_weights, device = self.net.device) * ((obtained_values - expected_values)**2).squeeze(1)).mean()
         else:
+            # loss = F.smooth_l1_loss(obtained_values, expected_values)
             loss = F.mse_loss(obtained_values, expected_values)
         
         if self.logger is not None:

rltorch/agents/QEPAgent.py

@@ -21,8 +21,20 @@ class QEPAgent:
         self.logger = logger
         self.policy_skip = 4
     
+    def save(self, file_location):
+        torch.save({
+            'policy': self.policy_net.model.state_dict(),
+            'value': self.value_net.model.state_dict()
+            }, file_location)
+    def load(self, file_location):
+        checkpoint = torch.load(file_location)
+        self.value_net.model.state_dict(checkpoint['value'])
+        self.value_net.model.to(self.value_net.device)
+        self.policy_net.model.state_dict(checkpoint['policy'])
+        self.policy_net.model.to(self.policy_net.device)
+        self.target_net.sync()
+
     def fitness(self, policy_net, value_net, state_batch):
-        # print("Worker started")
         batch_size = len(state_batch)
         action_probabilities = policy_net(state_batch)
         action_size = action_probabilities.shape[1]
@@ -44,8 +56,8 @@ class QEPAgent:
         value_importance = 1 - entropy_importance
         
         # entropy_loss = (action_probabilities * torch.log2(action_probabilities)).sum(1) # Standard entropy loss from information theory
-        entropy_loss = (action_probabilities - torch.tensor(1 / action_size).repeat(len(state_batch), action_size)).abs().sum(1)
+        entropy_loss = (action_probabilities - torch.tensor(1 / action_size, device = state_batch.device).repeat(len(state_batch), action_size)).abs().sum(1)
-        # print("END WORKER")
+        
         return (entropy_importance * entropy_loss - value_importance * obtained_values).mean().item()
         
 
@@ -119,7 +131,6 @@ class QEPAgent:
           self.policy_skip -= 1
           return
         self.policy_skip = 4
-
         if self.target_value_net is not None:
           self.policy_net.calc_gradients(self.target_value_net, state_batch)
         else:

rltorch/env/simulate.py

@@ -17,7 +17,7 @@ def simulateEnvEps(env, actor, config, total_episodes = 1, memory = None, logger
 
     if episode % config['print_stat_n_eps'] == 0:
       print("episode: {}/{}, score: {}"
-        .format(episode, total_episodes, episode_reward))
+        .format(episode, total_episodes, episode_reward), flush=True)
     
     if logger is not None:
       logger.append(name + '/EpisodeReward', episode_reward)
@@ -51,7 +51,7 @@ class EnvironmentRunSync():
       if done:
         if self.episode_num % self.config['print_stat_n_eps'] == 0:
           print("episode: {}/{}, score: {}"
-            .format(self.episode_num, self.config['total_training_episodes'], self.episode_reward))
+            .format(self.episode_num, self.config['total_training_episodes'], self.episode_reward), flush=True)
           
         if self.logwriter is not None:
           logger.append(self.name + '/EpisodeReward', self.episode_reward)
@@ -92,7 +92,7 @@ class EnvironmentEpisodeSync():
 
     if self.episode_num % self.config['print_stat_n_eps'] == 0:
       print("episode: {}/{}, score: {}"
-        .format(self.episode_num, self.config['total_training_episodes'], episodeReward))
+        .format(self.episode_num, self.config['total_training_episodes'], episodeReward), flush=True)
           
     if self.logwriter is not None:
       logger.append(self.name + '/EpisodeReward', episodeReward)

rltorch/env/wrappers.py

@@ -3,6 +3,111 @@ import torch
 from gym import spaces
 import cv2
 from collections import deque
+import numpy as np
+
+class EpisodicLifeEnv(gym.Wrapper):
+    def __init__(self, env=None):
+        """Make end-of-life == end-of-episode, but only reset on true game over.
+        Done by DeepMind for the DQN and co. since it helps value estimation.
+        """
+        super(EpisodicLifeEnv, self).__init__(env)
+        self.lives = 0
+        self.was_real_done = True
+        self.was_real_reset = False
+
+    def step(self, action):
+        obs, reward, done, info = self.env.step(action)
+        self.was_real_done = done
+        # check current lives, make loss of life terminal,
+        # then update lives to handle bonus lives
+        lives = self.env.unwrapped.ale.lives()
+        if lives < self.lives and lives > 0:
+            # for Qbert somtimes we stay in lives == 0 condtion for a few frames
+            # so its important to keep lives > 0, so that we only reset once
+            # the environment advertises done.
+            done = True
+        self.lives = lives
+        return obs, reward, done, info
+
+    def reset(self):
+        """Reset only when lives are exhausted.
+        This way all states are still reachable even though lives are episodic,
+        and the learner need not know about any of this behind-the-scenes.
+        """
+        if self.was_real_done:
+            obs = self.env.reset()
+            self.was_real_reset = True
+        else:
+            # no-op step to advance from terminal/lost life state
+            obs, _, _, _ = self.env.step(0)
+            self.was_real_reset = False
+        self.lives = self.env.unwrapped.ale.lives()
+        return obs
+
+
+class NoopResetEnv(gym.Wrapper):
+    def __init__(self, env=None, noop_max=30):
+        """Sample initial states by taking random number of no-ops on reset.
+        No-op is assumed to be action 0.
+        """
+        super(NoopResetEnv, self).__init__(env)
+        self.noop_max = noop_max
+        self.override_num_noops = None
+        assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
+
+    def step(self, action):
+        return self.env.step(action)
+
+    def reset(self):
+        """ Do no-op action for a number of steps in [1, noop_max]."""
+        self.env.reset()
+        if self.override_num_noops is not None:
+            noops = self.override_num_noops
+        else:
+            noops = np.random.randint(1, self.noop_max + 1)
+        assert noops > 0
+        obs = None
+        for _ in range(noops):
+            obs, _, done, _ = self.env.step(0)
+            if done:
+                obs = self.env.reset()
+        return obs
+
+
+class MaxAndSkipEnv(gym.Wrapper):
+    def __init__(self, env=None, skip=4):
+        """Return only every `skip`-th frame"""
+        super(MaxAndSkipEnv, self).__init__(env)
+        # most recent raw observations (for max pooling across time steps)
+        self._obs_buffer = deque(maxlen=2)
+        self._skip = skip
+
+    def step(self, action):
+        total_reward = 0.0
+        done = None
+        for _ in range(self._skip):
+            obs, reward, done, info = self.env.step(action)
+            self._obs_buffer.append(obs)
+            total_reward += reward
+            if done:
+                break
+
+        max_frame = np.max(np.stack(self._obs_buffer), axis=0)
+
+        return max_frame, total_reward, done, info
+
+    def reset(self):
+        """Clear past frame buffer and init. to first obs. from inner env."""
+        self._obs_buffer.clear()
+        obs = self.env.reset()
+        self._obs_buffer.append(obs)
+        return obs
+
+class ClippedRewardsWrapper(gym.RewardWrapper):
+    def reward(self, reward):
+        """Change all the positive rewards to 1, negative to -1 and keep zero."""
+        return np.sign(reward)
+
 
 # Mostly derived from OpenAI baselines
 class FireResetEnv(gym.Wrapper):
@@ -127,3 +232,28 @@ class TorchWrap(gym.Wrapper):
   def _convert(self, frame):
     frame = torch.from_numpy(frame).unsqueeze(0).float()
     return frame
+
+
+
+class ProcessFrame84(gym.ObservationWrapper):
+    def __init__(self, env=None):
+        super(ProcessFrame84, self).__init__(env)
+        self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1), dtype=np.uint8)
+
+    def observation(self, obs):
+        return ProcessFrame84.process(obs)
+
+    @staticmethod
+    def process(frame):
+        if frame.size == 210 * 160 * 3:
+            img = np.reshape(frame, [210, 160, 3]).astype(np.float32)
+        elif frame.size == 250 * 160 * 3:
+            img = np.reshape(frame, [250, 160, 3]).astype(np.float32)
+        else:
+            assert False, "Unknown resolution."
+        img = img[:, :, 0] * 0.299 + img[:, :, 1] * 0.587 + img[:, :, 2] * 0.114
+        resized_screen = cv2.resize(img, (84, 110), interpolation=cv2.INTER_AREA)
+        x_t = resized_screen[18:102, :]
+        x_t = np.reshape(x_t, [84, 84])
+        return x_t.astype(np.uint8)
+