Add short term memory

games/cartpole.py

@@ -12,10 +12,11 @@ class MuZeroConfig:
         self.observation_shape = 4  # Dimensions of the game observation
         self.action_space = [i for i in range(2)]  # Fixed list of all possible actions
         self.players = [i for i in range(1)]  # List of players
+        self.stacked_observations = 3  # Number of previous observation to add to the current observation
 
 
         ### Self-Play
-        self.num_actors = 10  # Number of simultaneous threads self-playing to feed the replay buffer
+        self.num_actors = 3  # Number of simultaneous threads self-playing to feed the replay buffer
         self.max_moves = 500  # Maximum number of moves if game is not finished before
         self.num_simulations = 50  # Number of futur moves self-simulated
         self.discount = 0.997  # Chronological discount of the reward

games/connect4.py

@@ -12,6 +12,7 @@ class MuZeroConfig:
         self.observation_shape = 6 * 7  # Dimensions of the game observation
         self.action_space = [i for i in range(7)]  # Fixed list of all possible actions
         self.players = [i for i in range(2)]  # List of players
+        self.stacked_observations = 2  # Number of previous observation to add to the current observation
 
 
         ### Self-Play
@@ -95,7 +96,7 @@ class Game:
             The new observation, the reward and a boolean if the game has ended.
         """
         observation, reward, done = self.env.step(action)
-        return numpy.array(observation).flatten(), reward, done
+        return observation, reward, done
 
     def to_play(self):
         """
@@ -126,7 +127,7 @@ class Game:
         Returns:
             Initial observation of the game.
         """
-        return numpy.array(self.env.reset()).flatten()
+        return self.env.reset()
 
     def close(self):
         """

games/lunarlander.py

@@ -12,11 +12,12 @@ class MuZeroConfig:
         self.observation_shape = 8  # Dimensions of the game observation
         self.action_space = [i for i in range(4)]  # Fixed list of all possible actions
         self.players = [i for i in range(1)]  # List of players
+        self.stacked_observations = 1  # Number of previous observation to add to the current observation
 
 
         ### Self-Play
         self.num_actors = 10  # Number of simultaneous threads self-playing to feed the replay buffer
-        self.max_moves = 1000  # Maximum number of moves if game is not finished before
+        self.max_moves = 800  # Maximum number of moves if game is not finished before
         self.num_simulations = 50  # Number of futur moves self-simulated
         self.discount = 0.997  # Chronological discount of the reward
         self.self_play_delay = 0  # Number of seconds to wait after each played game to adjust the self play / training ratio to avoid over/underfitting
@@ -97,7 +98,7 @@ class Game:
             The new observation, the reward and a boolean if the game has ended.
         """
         observation, reward, done, _ = self.env.step(action)
-        return numpy.array(observation).flatten(), reward/5, done
+        return observation, reward, done
 
     def to_play(self):
         """

models.py

@@ -2,9 +2,7 @@ import torch
 
 
 class FullyConnectedNetwork(torch.nn.Module):
-    def __init__(
-        self, input_size, layer_sizes, output_size, activation=torch.nn.Tanh()
-    ):
+    def __init__(self, input_size, layer_sizes, output_size, activation=None):
         super(FullyConnectedNetwork, self).__init__()
         sizes_list = layer_sizes.copy()
         sizes_list.insert(0, input_size)
@@ -32,6 +30,7 @@ class MuZeroNetwork(torch.nn.Module):
     def __init__(
         self,
         observation_size,
+        stacked_observations,
         action_space_size,
         encoding_size,
         hidden_layers,
@@ -42,28 +41,23 @@ class MuZeroNetwork(torch.nn.Module):
         self.full_support_size = 2 * support_size + 1
 
         self.representation_network = FullyConnectedNetwork(
-            observation_size, [], encoding_size
+            observation_size * (stacked_observations + 1), [], encoding_size
         )
 
         self.dynamics_encoded_state_network = FullyConnectedNetwork(
             encoding_size + self.action_space_size, hidden_layers, encoding_size
         )
-        # Gradient scaling (See paper appendix Training)
-        self.dynamics_encoded_state_network.register_backward_hook(
-            lambda module, grad_i, grad_o: (grad_i[0] * 0.5,)
-        )
         self.dynamics_reward_network = FullyConnectedNetwork(
             encoding_size + self.action_space_size,
             hidden_layers,
             self.full_support_size,
-            activation=None,
         )
 
         self.prediction_policy_network = FullyConnectedNetwork(
-            encoding_size, [], self.action_space_size, activation=None
+            encoding_size, [], self.action_space_size
         )
         self.prediction_value_network = FullyConnectedNetwork(
-            encoding_size, [], self.full_support_size, activation=None
+            encoding_size, [], self.full_support_size,
         )
 
     def prediction(self, encoded_state):
@@ -72,16 +66,18 @@ class MuZeroNetwork(torch.nn.Module):
         return policy_logit, value
 
     def representation(self, observation):
-        encoded_state = self.representation_network(observation)
-        # TODO: Try to remove tanh activation
+        encoded_state = self.representation_network(
+            observation.view(observation.shape[0], -1)
+        )
         # Scale encoded state between [0, 1] (See appendix paper Training)
-        encoded_state = (encoded_state - torch.min(encoded_state)) / (
-            torch.max(encoded_state) - torch.min(encoded_state)
+        encoded_state_diff = encoded_state - encoded_state.min(1, keepdim=True)[0]
+        encoded_state_normalized = (
+            encoded_state_diff / encoded_state_diff.max(1, keepdim=True)[0]
         )
         return encoded_state
 
     def dynamics(self, encoded_state, action):
-        # Stack encoded_state with one hot action (See paper appendix Network Architecture)
+        # Stack encoded_state with a game specific one hot encoded action (See paper appendix Network Architecture)
         action_one_hot = (
             torch.zeros((action.shape[0], self.action_space_size))
             .to(action.device)
@@ -91,20 +87,26 @@ class MuZeroNetwork(torch.nn.Module):
         x = torch.cat((encoded_state, action_one_hot), dim=1)
 
         next_encoded_state = self.dynamics_encoded_state_network(x)
-        # TODO: Try to remove tanh activation
+
         # Scale encoded state between [0, 1] (See paper appendix Training)
-        next_encoded_state = (next_encoded_state - torch.min(next_encoded_state)) / (
-            torch.max(next_encoded_state) - torch.min(next_encoded_state)
+        next_encoded_state_diff = (
+            next_encoded_state - next_encoded_state.min(1, keepdim=True)[0]
+        )
+        next_encoded_state_normalized = (
+            next_encoded_state_diff / next_encoded_state_diff.max(1, keepdim=True)[0]
         )
+
         reward = self.dynamics_reward_network(x)
-        return next_encoded_state, reward
+        return next_encoded_state_normalized, reward
 
     def initial_inference(self, observation):
         encoded_state = self.representation(observation)
         policy_logit, value = self.prediction(encoded_state)
         return (
             value,
-            torch.zeros(len(observation), self.full_support_size).to(observation.device),
+            torch.zeros(len(observation), self.full_support_size).to(
+                observation.device
+            ),
             policy_logit,
             encoded_state,
         )

muzero.py

@@ -54,10 +54,11 @@ class MuZero:
         # Initial weights used to initialize components
         self.muzero_weights = models.MuZeroNetwork(
             self.config.observation_shape,
+            self.config.stacked_observations,
             len(self.config.action_space),
             self.config.encoding_size,
             self.config.hidden_layers,
-            self.config.support_size
+            self.config.support_size,
         ).get_weights()
 
     def train(self):
@@ -137,7 +138,7 @@ class MuZero:
                 time.sleep(3)
         except KeyboardInterrupt as err:
             # Comment the line below to be able to stop the training but keep running
-            # raise err
+            raise err
             pass
         self.muzero_weights = ray.get(shared_storage_worker.get_weights.remote())
         ray.shutdown()
@@ -159,7 +160,9 @@ class MuZero:
         )
         test_rewards = []
         for _ in range(self.config.test_episodes):
-            history = ray.get(self_play_workers.play_game.remote(0, render, muzero_player))
+            history = ray.get(
+                self_play_workers.play_game.remote(0, render, muzero_player)
+            )
             test_rewards.append(sum(history.rewards))
         ray.shutdown()
         return test_rewards
@@ -183,7 +186,9 @@ if __name__ == "__main__":
 
     ## Test
     muzero.load_model()
+
     # Render some self-played games
     muzero.test(render=True, muzero_player=None)
+
     # Let user play against MuZero (MuZero is player 0 here)
     # muzero.test(render=True, muzero_player=0)

requirements.txt

 numpy
 torch
-ray
+tensorboard
 gym
 box2d-py
+ray
 psutil
 setproctitle
-tensorboard
+aiohttp
\ No newline at end of file
+grpcio
\ No newline at end of file

self_play.py

@@ -22,6 +22,7 @@ class SelfPlay:
         # Initialize the network
         self.model = models.MuZeroNetwork(
             self.config.observation_shape,
+            self.config.stacked_observations,
             len(self.config.action_space),
             self.config.encoding_size,
             self.config.hidden_layers,
@@ -66,6 +67,9 @@ class SelfPlay:
         """
         game_history = GameHistory()
         observation = self.game.reset()
+        observation = self.stack_previous_observations(
+            observation, game_history, self.config.stacked_observations
+        )
         game_history.observation_history.append(observation)
         done = False
 
@@ -75,7 +79,10 @@ class SelfPlay:
         with torch.no_grad():
             while not done and len(game_history.action_history) < self.config.max_moves:
                 current_player = self.game.to_play()
-                if play_against_human_player is None or play_against_human_player == current_player:
+                if (
+                    play_against_human_player is None
+                    or play_against_human_player == current_player
+                ):
                     root = MCTS(self.config).run(
                         self.model,
                         observation,
@@ -88,10 +95,19 @@ class SelfPlay:
 
                     observation, reward, done = self.game.step(action)
 
-                if play_against_human_player is not None and current_player != play_against_human_player:
-                    action = int(input("Enter the action of player {} : ".format(current_player)))
+                if (
+                    play_against_human_player is not None
+                    and current_player != play_against_human_player
+                ):
+                    action = int(
+                        input("Enter the action of player {} : ".format(current_player))
+                    )
                     observation, reward, done = self.game.step(action)
 
+                observation = self.stack_previous_observations(
+                    observation, game_history, self.config.stacked_observations,
+                )
+
                 if render:
                     print("Action : {}".format(action))
                     self.game.render()
@@ -105,6 +121,20 @@ class SelfPlay:
         return game_history
 
     @staticmethod
+    def stack_previous_observations(
+        observation, game_history, num_stacked_observations
+    ):
+        stacked_observations = [observation]
+        for i in range(num_stacked_observations):
+            try:
+                stacked_observations.append(
+                    game_history.observation_history[-(i + 1)][0]
+                )
+            except IndexError:
+                stacked_observations.append(numpy.zeros_like(observation))
+        return stacked_observations
+
+    @staticmethod
     def select_action(node, temperature):
         """
         Select action according to the vivist count distribution and the temperature.
@@ -151,7 +181,7 @@ class MCTS:
         """
         root = Node(0)
         observation = (
-            torch.from_numpy(observation)
+            torch.tensor(observation)
             .float()
             .unsqueeze(0)
             .to(next(model.parameters()).device)

trainer.py

@@ -21,6 +21,7 @@ class Trainer:
         # Initialize the network
         self.model = models.MuZeroNetwork(
             self.config.observation_shape,
+            self.config.stacked_observations,
             len(self.config.action_space),
             self.config.encoding_size,
             self.config.hidden_layers,
@@ -118,7 +119,7 @@ class Trainer:
         loss = (value_loss + reward_loss + policy_loss).mean()
 
         # Scale gradient by number of unroll steps (See paper Training appendix)
-        loss.register_hook(lambda grad: grad * 1 / self.config.num_unroll_steps)
+        loss.register_hook(lambda grad: grad / self.config.num_unroll_steps)
 
         # Optimize
         self.optimizer.zero_grad()
@@ -160,20 +161,22 @@ class Trainer:
         logits.scatter_(
             2, (floor + support_size).long().unsqueeze(-1), (1 - prob).unsqueeze(-1)
         )
-        indexes = (floor + support_size + 1)
+        indexes = floor + support_size + 1
         prob = prob.masked_fill_(2 * support_size < indexes, 0.0)
         indexes = indexes.masked_fill_(2 * support_size < indexes, 0.0)
-        logits.scatter_(
-            2, indexes.long().unsqueeze(-1), prob.unsqueeze(-1)
-        )
+        logits.scatter_(2, indexes.long().unsqueeze(-1), prob.unsqueeze(-1))
         return logits
 
     @staticmethod
     def loss_function(
         value, reward, policy_logits, target_value, target_reward, target_policy
     ):
-        # Cross-entropy had a better convergence than MSE 
+        # Cross-entropy had a better convergence than MSE
         value_loss = (-target_value * torch.nn.LogSoftmax(dim=1)(value)).sum(1).mean()
-        reward_loss = (-target_reward * torch.nn.LogSoftmax(dim=1)(reward)).sum(1).mean()
-        policy_loss = (-target_policy * torch.nn.LogSoftmax(dim=1)(policy_logits)).sum(1).mean()
+        reward_loss = (
+            (-target_reward * torch.nn.LogSoftmax(dim=1)(reward)).sum(1).mean()
+        )
+        policy_loss = (
+            (-target_policy * torch.nn.LogSoftmax(dim=1)(policy_logits)).sum(1).mean()
+        )
         return value_loss, reward_loss, policy_loss