DQN playing LunarLander-v2 from https://github.com/sgoodfriend/rl-algo-impls/tree/1d4094fbcc9082de7f53f4348dd4c7c354152907
9839b09
| import gym | |
| import numpy as np | |
| from typing import Optional, Tuple, Union | |
| ObsType = Union[np.ndarray, dict] | |
| ActType = Union[int, float, np.ndarray, dict] | |
| class NoRewardTimeout(gym.Wrapper): | |
| def __init__( | |
| self, env: gym.Env, n_timeout_steps: int, n_fire_steps: Optional[int] = None | |
| ) -> None: | |
| super().__init__(env) | |
| self.n_timeout_steps = n_timeout_steps | |
| self.n_fire_steps = n_fire_steps | |
| self.fire_act = None | |
| if n_fire_steps is not None: | |
| action_meanings = env.unwrapped.get_action_meanings() | |
| assert "FIRE" in action_meanings | |
| self.fire_act = action_meanings.index("FIRE") | |
| self.steps_since_reward = 0 | |
| self.episode_score = 0 | |
| self.episode_step_idx = 0 | |
| def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]: | |
| if self.steps_since_reward == self.n_fire_steps: | |
| assert self.fire_act is not None | |
| self.print_intervention("Force fire action") | |
| action = self.fire_act | |
| obs, rew, done, info = self.env.step(action) | |
| self.episode_score += rew | |
| self.episode_step_idx += 1 | |
| if rew != 0 or done: | |
| self.steps_since_reward = 0 | |
| else: | |
| self.steps_since_reward += 1 | |
| if self.steps_since_reward >= self.n_timeout_steps: | |
| self.print_intervention("Early terminate") | |
| done = True | |
| return obs, rew, done, info | |
| def reset(self, **kwargs) -> ObsType: | |
| self._reset_state() | |
| return self.env.reset(**kwargs) | |
| def _reset_state(self) -> None: | |
| self.steps_since_reward = 0 | |
| self.episode_score = 0 | |
| self.episode_step_idx = 0 | |
| def print_intervention(self, tag: str) -> None: | |
| print( | |
| f"{self.__class__.__name__}: {tag} | " | |
| f"Score: {self.episode_score} | " | |
| f"Length: {self.episode_step_idx}" | |
| ) | |