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ray-project--ray/rllib/examples/envs/classes/cartpole_sparse_rewards.py
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2026-07-13 13:17:40 +08:00

52 lines
1.6 KiB
Python

from copy import deepcopy
import gymnasium as gym
import numpy as np
from gymnasium.spaces import Box, Dict, Discrete
class CartPoleSparseRewards(gym.Env):
"""Wrapper for gym CartPole environment where reward is accumulated to the end."""
def __init__(self, config=None):
self.env = gym.make("CartPole-v1")
self.action_space = Discrete(2)
self.observation_space = Dict(
{
"obs": self.env.observation_space,
"action_mask": Box(
low=0, high=1, shape=(self.action_space.n,), dtype=np.int8
),
}
)
self.running_reward = 0
def reset(self, *, seed=None, options=None):
self.running_reward = 0
obs, infos = self.env.reset()
return {
"obs": obs,
"action_mask": np.array([1, 1], dtype=np.int8),
}, infos
def step(self, action):
obs, rew, terminated, truncated, info = self.env.step(action)
self.running_reward += rew
score = self.running_reward if terminated else 0
return (
{"obs": obs, "action_mask": np.array([1, 1], dtype=np.int8)},
score,
terminated,
truncated,
info,
)
def set_state(self, state):
self.running_reward = state[1]
self.env = deepcopy(state[0])
obs = np.array(list(self.env.unwrapped.state))
return {"obs": obs, "action_mask": np.array([1, 1], dtype=np.int8)}
def get_state(self):
return deepcopy(self.env), self.running_reward