[content] dopamine/discrete_domains/atari_lib.py (python)
from baselines.common import atari_wrappers
import cv2
import gin
import gym as legacy_gym
import gymnasium as gym
from gymnasium.spaces.box import Box
import numpy as np
import tensorflow as tf


NATURE_DQN_OBSERVATION_SHAPE = (84, 84)  # Size of downscaled Atari 2600 frame.
NATURE_DQN_DTYPE = tf.uint8  # DType of Atari 2600 observations.
NATURE_DQN_STACK_SIZE = 4  # Number of frames in the state stack.

DQNNetworkType = collections.namedtuple('dqn_network', ['q_values'])
RainbowNetworkType = collections.namedtuple(
    'c51_network', ['q_values', 'logits', 'probabilities']
)
ImplicitQuantileNetworkType = collections.namedtuple(
    'iqn_network', ['quantile_values', 'quantiles']
)




@gin.configurable
def create_atari_environment(
    game_name=None,
    sticky_actions=True,
    use_legacy_gym=False,
    use_ppo_preprocessing=False,
    continuous_action_threshold=None,
):
  """Wraps an Atari 2600 Gym environment with some basic preprocessing.

  This preprocessing matches the guidelines proposed in Machado et al. (2017),
  "Revisiting the Arcade Learning Environment: Evaluation Protocols and Open
  Problems for General Agents".

  The created environment is the Gym wrapper around the Arcade Learning
  Environment.

  The main choice available to the user is whether to use sticky actions or not.
  Sticky actions, as prescribed by Machado et al., cause actions to persist
  with some probability (0.25) when a new command is sent to the ALE. This
  can be viewed as introducing a mild form of stochasticity in the environment.
  We use them by default.

  Args:
    game_name: str, the name of the Atari 2600 domain.
    sticky_actions: bool, whether to use sticky_actions as per Machado et al.
    use_legacy_gym: bool, whether to use use the legacy Gym API.
    use_ppo_preprocessing: bool, whether to use preprocessing for PPO.
    continuous_action_threshold: Optional[float], if not None, will use CALE
      (the continuous version of the ALE) with this action threshold.

  Returns:
    An Atari 2600 environment with some standard preprocessing.
  """
  assert game_name is not None
  if use_legacy_gym:
    copy_roms(roms_dir)
    game_version = 'v0' if sticky_actions else 'v4'
    full_game_name = f'{game_name}NoFrameskip-{game_version}'
    env = legacy_gym.make(full_game_name)
  else:
    gym.register_envs(ale_py)
    full_game_name = f'ALE/{game_name}-v5'
    repeat_action_probability = 0.25 if sticky_actions else 0.0
    continuous = continuous_action_threshold is not None
    continuous_action_threshold = (
        0.0
        if continuous_action_threshold is None
        else continuous_action_threshold
    )
    env = gym.make(
        full_game_name,
        repeat_action_probability=repeat_action_probability,
        frameskip=1,
        max_num_frames_per_episode=100_000,
        continuous=continuous,
        continuous_action_threshold=continuous_action_threshold,
    )

  if use_ppo_preprocessing:
    env = atari_wrappers.NoopResetEnv(env, noop_max=30)
    env = atari_wrappers.MaxAndSkipEnv(env, skip=4)
    env = atari_wrappers.EpisodicLifeEnv(env)
    if 'FIRE' in env.unwrapped.get_action_meanings():
      env = atari_wrappers.FireResetEnv(env)
    env = atari_wrappers.ClipRewardEnv(env)
    env = gym.wrappers.ResizeObservation(env, (84, 84))
    env = gym.wrappers.GrayScaleObservation(env)
    env = gym.wrappers.FrameStack(env, 4)
    env = env.env  # Strip the TimeLimit wrapper
    env = GameOverWrapper(env)
  else:
    # Strip out the TimeLimit wrapper from Gym, which caps us at 100k frames. We
    # handle this time limit internally instead, which lets us cap at 108k
    # frames (30 minutes). The TimeLimit wrapper also plays poorly with saving
    # and restoring states.
    env = env.env
    env = AtariPreprocessing(env)
  return env


@gin.configurable(denylist=['variables'])
def maybe_transform_variable_names(variables, legacy_checkpoint_load=False):
  """Maps old variable names to the new ones.

  The resulting dictionary can be passed to the tf.compat.v1.train.Saver to load
  legacy checkpoints into Keras models.

  Args:
    variables: list, of all variables to be transformed.
    legacy_checkpoint_load: bool, if True the variable names are mapped to the
      legacy names as appeared in `tf.slim` based agents. Use this if you want
      to load checkpoints saved before tf.keras.Model upgrade.

  Returns:
    dict or None, of <new_names, var>.
  """
  logging.info('legacy_checkpoint_load: %s', legacy_checkpoint_load)
  if legacy_checkpoint_load:
    name_map = {}
    for var in variables:
      new_name = var.op.name.replace('bias', 'biases')
      new_name = new_name.replace('kernel', 'weights')
      name_map[new_name] = var
  else:
    name_map = None
  return name_map


class NatureDQNNetwork(tf.keras.Model):
  """The convolutional network used to compute the agent's Q-values."""

  def __init__(self, num_actions, name=None):
    """Creates the layers used for calculating Q-values.

    Args:
      num_actions: int, number of actions.
      name: str, used to create scope for network parameters.
    """
    super(NatureDQNNetwork, self).__init__(name=name)

    self.num_actions = num_actions
    # Defining layers.
    activation_fn = tf.keras.activations.relu
    # Setting names of the layers manually to make variable names more similar
    # with tf.slim variable names/checkpoints.
    self.conv1 = tf.keras.layers.Conv2D(
        32,
        [8, 8],
        strides=4,
        padding='same',
        activation=activation_fn,
        name='Conv',
    )
    self.conv2 = tf.keras.layers.Conv2D(
        64,
        [4, 4],
        strides=2,
        padding='same',
        activation=activation_fn,
        name='Conv',
    )
    self.conv3 = tf.keras.layers.Conv2D(
        64,
        [3, 3],
        strides=1,
        padding='same',
        activation=activation_fn,
        name='Conv',
    )
    self.flatten = tf.keras.layers.Flatten()
    self.dense1 = tf.keras.layers.Dense(
        512, activation=activation_fn, name='fully_connected'
    )
    self.dense2 = tf.keras.layers.Dense(num_actions, name='fully_connected')

  def call(self, state):
    """Creates the output tensor/op given the state tensor as input.

    See https://www.tensorflow.org/api_docs/python/tf/keras/Model for more
    information on this. Note that tf.keras.Model implements `call` which is
    wrapped by `__call__` function by tf.keras.Model.

    Parameters created here will have scope according to the `name` argument
    given at `.__init__()` call.
    Args:
      state: Tensor, input tensor.

    Returns:
      collections.namedtuple, output ops (graph mode) or output tensors (eager).
    """
    x = tf.cast(state, tf.float32)
    x = x / 255
    x = self.conv1(x)
    x = self.conv2(x)
    x = self.conv3(x)
    x = self.flatten(x)
    x = self.dense1(x)

    return DQNNetworkType(self.dense2(x))


class RainbowNetwork(tf.keras.Model):
  """The convolutional network used to compute agent's return distributions."""

  def __init__(self, num_actions, num_atoms, support, name=None):
    """Creates the layers used calculating return distributions.

    Args:
      num_actions: int, number of actions.
      num_atoms: int, the number of buckets of the value function distribution.
      support: tf.linspace, the support of the Q-value distribution.
      name: str, used to crete scope for network parameters.
    """
    super(RainbowNetwork, self).__init__(name=name)
    activation_fn = tf.keras.activations.relu
    self.num_actions = num_actions
    self.num_atoms = num_atoms
    self.support = support

    def kernel_initializer():
      return tf.keras.initializers.VarianceScaling(
          scale=1.0 / np.sqrt(3.0), mode='fan_in', distribution='uniform'
      )

    # Defining layers.
    self.conv1 = tf.keras.layers.Conv2D(
        32,
        [8, 8],
        strides=4,
        padding='same',
        activation=activation_fn,
        kernel_initializer=kernel_initializer(),
        name='Conv',
    )
    self.conv2 = tf.keras.layers.Conv2D(
        64,
        [4, 4],
        strides=2,
        padding='same',
        activation=activation_fn,
        kernel_initializer=kernel_initializer(),
        name='Conv',
    )
    self.conv3 = tf.keras.layers.Conv2D(
        64,
        [3, 3],
        strides=1,
        padding='same',
        activation=activation_fn,
        kernel_initializer=kernel_initializer(),
        name='Conv',
    )
    self.flatten = tf.keras.layers.Flatten()
    self.dense1 = tf.keras.layers.Dense(
        512,
        activation=activation_fn,
        kernel_initializer=kernel_initializer(),
        name='fully_connected',
    )
    self.dense2 = tf.keras.layers.Dense(
        num_actions * num_atoms,
        kernel_initializer=kernel_initializer(),
        name='fully_connected',
    )

  def call(self, state):
    """Creates the output tensor/op given the state tensor as input.

    See https://www.tensorflow.org/api_docs/python/tf/keras/Model for more
    information on this. Note that tf.keras.Model implements `call` which is
    wrapped by `__call__` function by tf.keras.Model.

    Args:
      state: Tensor, input tensor.

    Returns:
      collections.namedtuple, output ops (graph mode) or output tensors (eager).
    """
    x = tf.cast(state, tf.float32)
    x = x / 255
    x = self.conv1(x)
    x = self.conv2(x)
    x = self.conv3(x)
    x = self.flatten(x)
    x = self.dense1(x)
    x = self.dense2(x)
    logits = tf.reshape(x, [-1, self.num_actions, self.num_atoms])
    probabilities = tf.keras.activations.softmax(logits)
    q_values = tf.reduce_sum(self.support * probabilities, axis=2)
    return RainbowNetworkType(q_values, logits, probabilities)


class ImplicitQuantileNetwork(tf.keras.Model):
  """The Implicit Quantile Network (Dabney et al., 2018).."""

  def __init__(self, num_actions, quantile_embedding_dim, name=None):
    """Creates the layers used calculating quantile values.

    Args:
      num_actions: int, number of actions.
      quantile_embedding_dim: int, embedding dimension for the quantile input.
      name: str, used to create scope for network parameters.
    """
    super(ImplicitQuantileNetwork, self).__init__(name=name)
    self.num_actions = num_actions
    self.quantile_embedding_dim = quantile_embedding_dim
    # We need the activation function during `call`, therefore set the field.
    self.activation_fn = tf.keras.activations.relu

    def kernel_initializer():
      return tf.keras.initializers.VarianceScaling(
          scale=1.0 / np.sqrt(3.0), mode='fan_in', distribution='uniform'
      )

    self.kernel_initializer = kernel_initializer
    # Defining layers.
    self.conv1 = tf.keras.layers.Conv2D(
        32,
        [8, 8],
        strides=4,
        padding='same',
        activation=self.activation_fn,
        kernel_initializer=kernel_initializer(),
        name='Conv',
    )
    self.conv2 = tf.keras.layers.Conv2D(
        64,
        [4, 4],
        strides=2,
        padding='same',
        activation=self.activation_fn,
        kernel_initializer=kernel_initializer(),
        name='Conv',
    )
    self.conv3 = tf.keras.layers.Conv2D(
        64,
        [3, 3],
        strides=1,
        padding='same',
        activation=self.activation_fn,
        kernel_initializer=kernel_initializer(),
        name='Conv',
    )
    self.flatten = tf.keras.layers.Flatten()
    self.dense1 = tf.keras.layers.Dense(
        512,
        activation=self.activation_fn,
        kernel_initializer=kernel_initializer(),
        name='fully_connected',
    )
    self.dense2 = tf.keras.layers.Dense(
        num_actions,
        kernel_initializer=kernel_initializer(),
        name='fully_connected',
    )

  def call(self, state, num_quantiles):
    """Creates the output tensor/op given the state tensor as input.

    See https://www.tensorflow.org/api_docs/python/tf/keras/Model for more
    information on this. Note that tf.keras.Model implements `call` which is
    wrapped by `__call__` function by tf.keras.Model.

    Args:
      state: `tf.Tensor`, contains the agent's current state.
      num_quantiles: int, number of quantile inputs.

    Returns:
      collections.namedtuple, that contains (quantile_values, quantiles).
    """
    batch_size = state.get_shape().as_list()[0]
    x = tf.cast(state, tf.float32)
    x = x / 255
    x = self.conv1(x)
    x = self.conv2(x)
    x = self.conv3(x)
    x = self.flatten(x)
    state_vector_length = x.get_shape().as_list()[-1]
    state_net_tiled = tf.tile(x, [num_quantiles, 1])
    quantiles_shape = [num_quantiles * batch_size, 1]
    quantiles = tf.random.uniform(
        quantiles_shape, minval=0, maxval=1, dtype=tf.float32
    )
    quantile_net = tf.tile(quantiles, [1, self.quantile_embedding_dim])
    pi = tf.constant(math.pi)
    quantile_net = (
        tf.cast(tf.range(1, self.quantile_embedding_dim + 1, 1), tf.float32)
        * pi
        * quantile_net
    )
    quantile_net = tf.cos(quantile_net)
    # Create the quantile layer in the first call. This is because
    # number of output units depends on the input shape. Therefore, we can only
    # create the layer during the first forward call, not during `.__init__()`.
    if not hasattr(self, 'dense_quantile'):
      self.dense_quantile = tf.keras.layers.Dense(
          state_vector_length,
          activation=self.activation_fn,
          kernel_initializer=self.kernel_initializer(),
      )
    quantile_net = self.dense_quantile(quantile_net)
    x = tf.multiply(state_net_tiled, quantile_net)
    x = self.dense1(x)
    quantile_values = self.dense2(x)
    return ImplicitQuantileNetworkType(quantile_values, quantiles)


@gin.configurable
class AtariPreprocessing(object):
  """A class implementing image preprocessing for Atari 2600 agents.

  Specifically, this provides the following subset from the JAIR paper
  (Bellemare et al., 2013) and Nature DQN paper (Mnih et al., 2015):

    * Frame skipping (defaults to 4).
    * Terminal signal when a life is lost (off by default).
    * Grayscale and max-pooling of the last two frames.
    * Downsample the screen to a square image (defaults to 84x84).

  More generally, this class follows the preprocessing guidelines set down in
  Machado et al. (2018), "Revisiting the Arcade Learning Environment:
  Evaluation Protocols and Open Problems for General Agents".
  """

  def __init__(
      self,
      environment,
      frame_skip=4,
      terminal_on_life_loss=False,
      screen_size=84,
      use_legacy_gym=False,
  ):
    """Constructor for an Atari 2600 preprocessor.

    Args:
      environment: Gym environment whose observations are preprocessed.
      frame_skip: int, the frequency at which the agent experiences the game.
      terminal_on_life_loss: bool, If True, the step() method returns
        is_terminal=True whenever a life is lost. See Mnih et al. 2015.
      screen_size: int, size of a resized Atari 2600 frame.
      use_legacy_gym: bool, whether to use legacy Gym or new Gymnasium API.

    Raises:
      ValueError: if frame_skip or screen_size are not strictly positive.
    """
    if frame_skip <= 0:
      raise ValueError(
          f'Frame skip should be strictly positive, got {frame_skip}'
      )
    if screen_size <= 0:
      raise ValueError(
          f'Target screen size should be strictly positive, got {screen_size}'
      )

    self.environment = environment
    self.terminal_on_life_loss = terminal_on_life_loss
    self.frame_skip = frame_skip
    self.screen_size = screen_size
    self._use_legacy_gym = use_legacy_gym

    obs_dims = self.environment.observation_space
    # Stores temporary observations used for pooling over two successive
    # frames.
    self.screen_buffer = [
        np.empty((obs_dims.shape[0], obs_dims.shape[1]), dtype=np.uint8),
        np.empty((obs_dims.shape[0], obs_dims.shape[1]), dtype=np.uint8),
    ]

    self.game_over = False
    self.lives = 0  # Will need to be set by reset().

  @property
  def observation_space(self):
    # Return the observation space adjusted to match the shape of the processed
    # observations.
    return Box(
        low=0,
        high=255,
        shape=(self.screen_size, self.screen_size, 1),
        dtype=np.uint8,
    )

  @property
  def action_space(self):
    return self.environment.action_space

  @property
  def reward_range(self):
    return self.environment.reward_range

  @property
  def metadata(self):
    return self.environment.metadata

  def close(self):
    return self.environment.close()

  def reset(self):
    """Resets the environment.

    Returns:
      observation: numpy array, the initial observation emitted by the
        environment.
    """
    self.environment.reset()
    self.lives = self.environment.env.ale.lives()
    self._fetch_grayscale_observation(self.screen_buffer[0])
    self.screen_buffer[1].fill(0)
    return self._pool_and_resize()

  def render(self, mode):
    """Renders the current screen, before preprocessing.

    This calls the Gym API's render() method.

    Args:
      mode: Mode argument for the environment's render() method. Valid values
        (str) are: 'rgb_array': returns the raw ALE image. 'human': renders to
        display via the Gym renderer.

    Returns:
      if mode='rgb_array': numpy array, the most recent screen.
      if mode='human': bool, whether the rendering was successful.
    """
    return self.environment.render(mode)

  def step(self, action):
    """Applies the given action in the environment.

    Remarks:

      * If a terminal state (from life loss or episode end) is reached, this may
        execute fewer than self.frame_skip steps in the environment.
      * Furthermore, in this case the returned observation may not contain valid
        image data and should be ignored.

    Args:
      action: The action to be executed.

    Returns:
      observation: numpy array, the observation following the action.
      reward: float, the reward following the action.
      is_terminal: bool, whether the environment has reached a terminal state.
        This is true when a life is lost and terminal_on_life_loss, or when the
        episode is over.
      info: Gym API's info data structure.
    """
    accumulated_reward = 0.0

    for time_step in range(self.frame_skip):
      # We bypass the Gym observation altogether and directly fetch the
      # grayscale image from the ALE. This is a little faster.
      if self._use_legacy_gym:
        _, reward, game_over, info = self.environment.step(action)
      else:
        _, reward, terminated, truncated, info = self.environment.step(action)
        game_over = terminated or truncated
      accumulated_reward += reward

      if self.terminal_on_life_loss:
        new_lives = self.environment.env.ale.lives()
        is_terminal = game_over or new_lives < self.lives
        self.lives = new_lives
      else:
        is_terminal = game_over

      # We max-pool over the last two frames, in grayscale.
      if time_step >= self.frame_skip - 2:
        t = time_step - (self.frame_skip - 2)
        self._fetch_grayscale_observation(self.screen_buffer[t])

      if is_terminal:
        break

    # Pool the last two observations.
    observation = self._pool_and_resize()

    self.game_over = game_over
    return observation, accumulated_reward, is_terminal, info

  def _fetch_grayscale_observation(self, output):
    """Returns the current observation in grayscale.

    The returned observation is stored in 'output'.

    Args:
      output: numpy array, screen buffer to hold the returned observation.

    Returns:
      observation: numpy array, the current observation in grayscale.
    """
    self.environment.env.ale.getScreenGrayscale(output)
    return output

  def _pool_and_resize(self):
    """Transforms two frames into a Nature DQN observation.

    For efficiency, the transformation is done in-place in self.screen_buffer.

    Returns:
      transformed_screen: numpy array, pooled, resized screen.
    """
    # Pool if there are enough screens to do so.
    if self.frame_skip > 1:
      np.maximum(
          self.screen_buffer[0],
          self.screen_buffer[1],
          out=self.screen_buffer[0],
      )

    transformed_image = cv2.resize(
        self.screen_buffer[0],
        (self.screen_size, self.screen_size),
        interpolation=cv2.INTER_AREA,
    )
    int_image = np.asarray(transformed_image, dtype=np.uint8)
    return np.expand_dims(int_image, axis=2)


@gin.configurable
class GameOverWrapper(object):
  """A Wrapper class around Gym environments adding game over signal."""

  def __init__(self, environment):
    self.environment = environment
    self.game_over = False

  @property
  def observation_space(self):
    return self.environment.observation_space

  @property
  def action_space(self):
    return self.environment.action_space

  def reset(self):
    return self.environment.reset()

  def step(self, action):
    observation, reward, game_over, info = self.environment.step(action)
    truncated = info.get('TimeLimit.truncated', False)
    self.game_over = game_over and not truncated
    return observation, reward, self.game_over, info