File size: 6,845 Bytes
f540c0e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
import gym
import numpy as np
import torch
from abc import abstractmethod
from gym.spaces import Box, Discrete, Space
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
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
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: Sequence[int],
v_hidden_sizes: Sequence[int],
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,
**kwargs,
) -> None:
super().__init__(env, **kwargs)
activation = ACTIVATION[activation_fn]
observation_space = env.observation_space
self.action_space = env.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,
)
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
)
|