File size: 1,400 Bytes
6462893 |
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 |
import numpy as np
import os
import torch
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
from typing import Optional, Sequence, TypeVar
from dqn.q_net import QNetwork
from shared.policy.policy import Policy
DQNPolicySelf = TypeVar("DQNPolicySelf", bound="DQNPolicy")
class DQNPolicy(Policy):
def __init__(
self,
env: VecEnv,
hidden_sizes: Sequence[int] = [],
cnn_feature_dim: int = 512,
cnn_style: str = "nature",
cnn_layers_init_orthogonal: Optional[bool] = None,
**kwargs,
) -> None:
super().__init__(env, **kwargs)
self.q_net = QNetwork(
env.observation_space,
env.action_space,
hidden_sizes,
cnn_feature_dim=cnn_feature_dim,
cnn_style=cnn_style,
cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
)
def act(
self, obs: VecEnvObs, eps: float = 0, deterministic: bool = True
) -> np.ndarray:
assert eps == 0 if deterministic else eps >= 0
if not deterministic and np.random.random() < eps:
return np.array(
[self.env.action_space.sample() for _ in range(self.env.num_envs)]
)
else:
o = self._as_tensor(obs)
with torch.no_grad():
return self.q_net(o).argmax(axis=1).cpu().numpy()
|