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import numpy as np
from rl_algo_impls.runner.config import Config
from rl_algo_impls.shared.callbacks import Callback
from rl_algo_impls.wrappers.vectorable_wrapper import VecEnv
class MicrortsRewardDecayCallback(Callback):
def __init__(
self,
config: Config,
env: VecEnv,
start_timesteps: int = 0,
) -> None:
super().__init__()
from gym_microrts.envs.vec_env import MicroRTSGridModeVecEnv
unwrapped = env.unwrapped
assert isinstance(unwrapped, MicroRTSGridModeVecEnv)
self.microrts_env = unwrapped
self.base_reward_weights = self.microrts_env.reward_weight
self.total_train_timesteps = config.n_timesteps
self.timesteps_elapsed = start_timesteps
def on_step(self, timesteps_elapsed: int = 1) -> bool:
super().on_step(timesteps_elapsed)
progress = self.timesteps_elapsed / self.total_train_timesteps
# Decay all rewards except WinLoss
reward_weights = self.base_reward_weights * np.array(
[1] + [1 - progress] * (len(self.base_reward_weights) - 1)
)
self.microrts_env.reward_weight = reward_weights
return True
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