A2C playing Walker2DBulletEnv-v0 from https://github.com/sgoodfriend/rl-algo-impls/tree/2067e21d62fff5db60168687e7d9e89019a8bfc0
9c82d00
import gym | |
import numpy as np | |
from typing import Optional, Tuple, Union | |
from rl_algo_impls.wrappers.vectorable_wrapper import VecotarableWrapper | |
ObsType = Union[np.ndarray, dict] | |
ActType = Union[int, float, np.ndarray, dict] | |
class NoRewardTimeout(VecotarableWrapper): | |
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}" | |
) | |