import gym import numpy as np import torch from abc import abstractmethod from gym.spaces import Box, Discrete, Space from typing import NamedTuple, Optional, Sequence, Tuple, TypeVar from shared.module.feature_extractor import FeatureExtractor from shared.policy.actor import PiForward, StateDependentNoiseActorHead, actor_head from shared.policy.critic import CriticHead from shared.policy.policy import ACTIVATION, Policy from wrappers.vectorable_wrapper import ( VecEnv, VecEnvObs, single_observation_space, single_action_space, ) class Step(NamedTuple): a: np.ndarray v: np.ndarray logp_a: np.ndarray clamped_a: np.ndarray class ACForward(NamedTuple): logp_a: torch.Tensor entropy: torch.Tensor v: torch.Tensor FEAT_EXT_FILE_NAME = "feat_ext.pt" V_FEAT_EXT_FILE_NAME = "v_feat_ext.pt" PI_FILE_NAME = "pi.pt" V_FILE_NAME = "v.pt" ActorCriticSelf = TypeVar("ActorCriticSelf", bound="ActorCritic") def clamp_actions( actions: np.ndarray, action_space: gym.Space, squash_output: bool ) -> np.ndarray: if isinstance(action_space, Box): low, high = action_space.low, action_space.high # type: ignore if squash_output: # Squashed output is already between -1 and 1. Rescale if the actual # output needs to something other than -1 and 1 return low + 0.5 * (actions + 1) * (high - low) else: return np.clip(actions, low, high) return actions def default_hidden_sizes(obs_space: Space) -> Sequence[int]: if isinstance(obs_space, Box): if len(obs_space.shape) == 3: # By default feature extractor to output has no hidden layers return [] elif len(obs_space.shape) == 1: return [64, 64] else: raise ValueError(f"Unsupported observation space: {obs_space}") elif isinstance(obs_space, Discrete): return [64] else: raise ValueError(f"Unsupported observation space: {obs_space}") class OnPolicy(Policy): @abstractmethod def value(self, obs: VecEnvObs) -> np.ndarray: ... @abstractmethod def step(self, obs: VecEnvObs) -> Step: ... class ActorCritic(OnPolicy): def __init__( self, env: VecEnv, pi_hidden_sizes: Optional[Sequence[int]] = None, v_hidden_sizes: Optional[Sequence[int]] = None, init_layers_orthogonal: bool = True, activation_fn: str = "tanh", log_std_init: float = -0.5, use_sde: bool = False, full_std: bool = True, squash_output: bool = False, share_features_extractor: bool = True, cnn_feature_dim: int = 512, cnn_style: str = "nature", cnn_layers_init_orthogonal: Optional[bool] = None, impala_channels: Sequence[int] = (16, 32, 32), **kwargs, ) -> None: super().__init__(env, **kwargs) observation_space = single_observation_space(env) action_space = single_action_space(env) pi_hidden_sizes = ( pi_hidden_sizes if pi_hidden_sizes is not None else default_hidden_sizes(observation_space) ) v_hidden_sizes = ( v_hidden_sizes if v_hidden_sizes is not None else default_hidden_sizes(observation_space) ) activation = ACTIVATION[activation_fn] self.action_space = action_space self.squash_output = squash_output self.share_features_extractor = share_features_extractor self._feature_extractor = FeatureExtractor( observation_space, activation, init_layers_orthogonal=init_layers_orthogonal, cnn_feature_dim=cnn_feature_dim, cnn_style=cnn_style, cnn_layers_init_orthogonal=cnn_layers_init_orthogonal, impala_channels=impala_channels, ) self._pi = actor_head( self.action_space, (self._feature_extractor.out_dim,) + tuple(pi_hidden_sizes), init_layers_orthogonal, activation, log_std_init=log_std_init, use_sde=use_sde, full_std=full_std, squash_output=squash_output, ) if not share_features_extractor: self._v_feature_extractor = FeatureExtractor( observation_space, activation, init_layers_orthogonal=init_layers_orthogonal, cnn_feature_dim=cnn_feature_dim, cnn_style=cnn_style, cnn_layers_init_orthogonal=cnn_layers_init_orthogonal, ) v_hidden_sizes = (self._v_feature_extractor.out_dim,) + tuple( v_hidden_sizes ) else: self._v_feature_extractor = None v_hidden_sizes = (self._feature_extractor.out_dim,) + tuple(v_hidden_sizes) self._v = CriticHead( hidden_sizes=v_hidden_sizes, activation=activation, init_layers_orthogonal=init_layers_orthogonal, ) def _pi_forward( self, obs: torch.Tensor, action: Optional[torch.Tensor] = None ) -> Tuple[PiForward, torch.Tensor]: p_fe = self._feature_extractor(obs) pi_forward = self._pi(p_fe, action) return pi_forward, p_fe def _v_forward(self, obs: torch.Tensor, p_fc: torch.Tensor) -> torch.Tensor: v_fe = self._v_feature_extractor(obs) if self._v_feature_extractor else p_fc return self._v(v_fe) def forward(self, obs: torch.Tensor, action: torch.Tensor) -> ACForward: (_, logp_a, entropy), p_fc = self._pi_forward(obs, action) v = self._v_forward(obs, p_fc) assert logp_a is not None assert entropy is not None return ACForward(logp_a, entropy, v) def value(self, obs: VecEnvObs) -> np.ndarray: o = self._as_tensor(obs) with torch.no_grad(): fe = ( self._v_feature_extractor(o) if self._v_feature_extractor else self._feature_extractor(o) ) v = self._v(fe) return v.cpu().numpy() def step(self, obs: VecEnvObs) -> Step: o = self._as_tensor(obs) with torch.no_grad(): (pi, _, _), p_fc = self._pi_forward(o) a = pi.sample() logp_a = pi.log_prob(a) v = self._v_forward(o, p_fc) a_np = a.cpu().numpy() clamped_a_np = clamp_actions(a_np, self.action_space, self.squash_output) return Step(a_np, v.cpu().numpy(), logp_a.cpu().numpy(), clamped_a_np) def act(self, obs: np.ndarray, deterministic: bool = True) -> np.ndarray: if not deterministic: return self.step(obs).clamped_a else: o = self._as_tensor(obs) with torch.no_grad(): (pi, _, _), _ = self._pi_forward(o) a = pi.mode return clamp_actions(a.cpu().numpy(), self.action_space, self.squash_output) def load(self, path: str) -> None: super().load(path) self.reset_noise() def reset_noise(self, batch_size: Optional[int] = None) -> None: if isinstance(self._pi, StateDependentNoiseActorHead): self._pi.sample_weights( batch_size=batch_size if batch_size else self.env.num_envs )