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| Original file line number | Diff line number | Diff line change |
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| import datetime | ||
| import os | ||
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| import cv2 | ||
| import gym | ||
| import numpy | ||
| import torch | ||
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| from .abstract_game import AbstractGame | ||
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| class MuZeroConfig: | ||
| def __init__(self): | ||
| self.seed = 0 # Seed for numpy, torch and the game | ||
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| ### Game | ||
| self.observation_shape = (3, 96, 96) # Dimensions of the game observation, must be 3D (channel, height, width). For a 1D array, please reshape it to (1, 1, length of array) | ||
| self.action_space = [i for i in range(4)] # Fixed list of all possible actions. You should only edit the length | ||
| self.players = [i for i in range(1)] # List of players. You should only edit the length | ||
| self.stacked_observations = 2 # Number of previous observation and previous actions to add to the current observation | ||
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| ### Self-Play | ||
| self.num_actors = 2 # 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 = 20 # Number of future moves self-simulated | ||
| self.discount = 0.997 # Chronological discount of the reward | ||
| self.temperature_threshold = 500 # Number of moves before dropping temperature to 0 (ie playing according to the max) | ||
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| # Root prior exploration noise | ||
| self.root_dirichlet_alpha = 0.25 | ||
| self.root_exploration_fraction = 0.25 | ||
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| # UCB formula | ||
| self.pb_c_base = 19652 | ||
| self.pb_c_init = 1.25 | ||
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| ### Network | ||
| self.network = "resnet" # "resnet" / "fullyconnected" | ||
| self.support_size = 10 # Value and reward are scaled (with almost sqrt) and encoded on a vector with a range of -support_size to support_size | ||
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| # Residual Network | ||
| self.downsample = True # Downsample observations before representation network (See paper appendix Network Architecture) | ||
| self.blocks = 1 # Number of blocks in the ResNet | ||
| self.channels = 128 # Number of channels in the ResNet | ||
| self.reduced_channels = 16 # Number of channels before heads of dynamic and prediction networks | ||
| self.resnet_fc_reward_layers = [16] # Define the hidden layers in the reward head of the dynamic network | ||
| self.resnet_fc_value_layers = [16] # Define the hidden layers in the value head of the prediction network | ||
| self.resnet_fc_policy_layers = [16] # Define the hidden layers in the policy head of the prediction network | ||
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| # Fully Connected Network | ||
| self.encoding_size = 10 | ||
| self.fc_reward_layers = [16] # Define the hidden layers in the reward network | ||
| self.fc_value_layers = [] # Define the hidden layers in the value network | ||
| self.fc_policy_layers = [] # Define the hidden layers in the policy network | ||
| self.fc_representation_layers = [] # Define the hidden layers in the representation network | ||
| self.fc_dynamics_layers = [16] # Define the hidden layers in the dynamics network | ||
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| ### Training | ||
| self.results_path = os.path.join(os.path.dirname(__file__), "../results", os.path.basename(__file__)[:-3], datetime.datetime.now().strftime("%Y-%m-%d--%H-%M-%S")) # Path to store the model weights and TensorBoard logs | ||
| self.training_steps = 5000 # Total number of training steps (ie weights update according to a batch) | ||
| self.batch_size = 128 # Number of parts of games to train on at each training step | ||
| self.num_unroll_steps = 10 # Number of game moves to keep for every batch element | ||
| self.checkpoint_interval = 10 # Number of training steps before using the model for sef-playing | ||
| self.window_size = 1000 # Number of self-play games to keep in the replay buffer | ||
| self.td_steps = 50 # Number of steps in the future to take into account for calculating the target value | ||
| self.value_loss_weight = 1 # Scale the value loss to avoid overfitting of the value function, paper recommends 0.25 (See paper appendix Reanalyze) | ||
| self.training_device = "cuda" if torch.cuda.is_available() else "cpu" # Train on GPU if available | ||
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| self.optimizer = "Adam" # "Adam" or "SGD". Paper uses SGD | ||
| self.weight_decay = 1e-4 # L2 weights regularization | ||
| self.momentum = 0.9 # Used only if optimizer is SGD | ||
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| # Prioritized Replay (See paper appendix Training) | ||
| self.PER = True # Select in priority the elements in the replay buffer which are unexpected for the network | ||
| self.PER_alpha = 0.5 # How much prioritization is used, 0 corresponding to the uniform case, paper suggests 1 | ||
| self.PER_beta = 1.0 | ||
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| # Exponential learning rate schedule | ||
| self.lr_init = 0.05 # Initial learning rate | ||
| self.lr_decay_rate = 0.9 # Set it to 1 to use a constant learning rate | ||
| self.lr_decay_steps = 1000 | ||
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| ## Adjust the self play / training ratio to avoid over/underfitting | ||
| self.self_play_delay = 0 # Number of seconds to wait after each played game | ||
| self.training_delay = 0 # Number of seconds to wait after each training step | ||
| self.ratio = None # Desired self played games per training step ratio. Equivalent to a synchronous version, training can take much longer. Set it to None to disable it | ||
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| def visit_softmax_temperature_fn(self, trained_steps): | ||
| """ | ||
| Parameter to alter the visit count distribution to ensure that the action selection becomes greedier as training progresses. | ||
| The smaller it is, the more likely the best action (ie with the highest visit count) is chosen. | ||
| Returns: | ||
| Positive float. | ||
| """ | ||
| if trained_steps < 0.5 * self.training_steps: | ||
| return 1.0 | ||
| elif trained_steps < 0.75 * self.training_steps: | ||
| return 0.5 | ||
| else: | ||
| return 0.25 | ||
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| class Game(AbstractGame): | ||
| """ | ||
| Game wrapper. | ||
| """ | ||
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| def __init__(self, seed=None): | ||
| self.env = gym.make("Breakout-v4") | ||
| if seed is not None: | ||
| self.env.seed(seed) | ||
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| def step(self, action): | ||
| """ | ||
| Apply action to the game. | ||
| Args: | ||
| action : action of the action_space to take. | ||
| Returns: | ||
| The new observation, the reward and a boolean if the game has ended. | ||
| """ | ||
| observation, reward, done, _ = self.env.step(action) | ||
| observation = cv2.resize(observation, (96, 96), interpolation=cv2.INTER_AREA) | ||
| observation = numpy.asarray(observation, dtype=numpy.float32) / 255.0 | ||
| observation = numpy.moveaxis(observation, -1, 0) | ||
| return observation, reward, done | ||
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| def to_play(self): | ||
| """ | ||
| Return the current player. | ||
| Returns: | ||
| The current player, it should be an element of the players list in the config. | ||
| """ | ||
| return 0 | ||
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| def legal_actions(self): | ||
| """ | ||
| Should return the legal actions at each turn, if it is not available, it can return | ||
| the whole action space. At each turn, the game have to be able to handle one of returned actions. | ||
| For complex game where calculating legal moves is too long, the idea is to define the legal actions | ||
| equal to the action space but to return a negative reward if the action is illegal. | ||
| Returns: | ||
| An array of integers, subset of the action space. | ||
| """ | ||
| return [i for i in range(4)] | ||
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| def reset(self): | ||
| """ | ||
| Reset the game for a new game. | ||
| Returns: | ||
| Initial observation of the game. | ||
| """ | ||
| observation = self.env.reset() | ||
| observation = cv2.resize(observation, (96, 96), interpolation=cv2.INTER_AREA) | ||
| observation = numpy.asarray(observation, dtype=numpy.float32) / 255.0 | ||
| observation = numpy.moveaxis(observation, -1, 0) | ||
| return observation | ||
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| def close(self): | ||
| """ | ||
| Properly close the game. | ||
| """ | ||
| self.env.close() | ||
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| def render(self): | ||
| """ | ||
| Display the game observation. | ||
| """ | ||
| self.env.render() | ||
| input("Press enter to take a step ") | ||
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| def human_to_action(self): | ||
| """ | ||
| For multiplayer games, ask the user for a legal action | ||
| and return the corresponding action number. | ||
| Returns: | ||
| An integer from the action space. | ||
| """ | ||
| pass | ||
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| def action_to_string(self, action_number): | ||
| """ | ||
| Convert an action number to a string representing the action. | ||
| Args: | ||
| action_number: an integer from the action space. | ||
| Returns: | ||
| String representing the action. | ||
| """ | ||
| return print(action_number) |
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