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import numpy as np
import os
import torch
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
from typing import 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] = [],
**kwargs,
) -> None:
super().__init__(env, **kwargs)
self.q_net = QNetwork(env.observation_space, env.action_space, hidden_sizes)
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()
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