wehub-resource-sync
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
import gymnasium as gym
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import numpy as np
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from ray.rllib.env.multi_agent_env import MultiAgentEnv
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class DebugCounterEnv(gym.Env):
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"""Simple Env that yields a ts counter as observation (0-based).
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Actions have no effect.
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The episode length is always 15.
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Reward is always: current ts % 3.
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"""
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def __init__(self, config=None):
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config = config or {}
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self.action_space = gym.spaces.Discrete(2)
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self.observation_space = gym.spaces.Box(0, 100, (1,), dtype=np.float32)
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self.start_at_t = int(config.get("start_at_t", 0))
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self.i = self.start_at_t
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def reset(self, *, seed=None, options=None):
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self.i = self.start_at_t
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return self._get_obs(), {}
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def step(self, action):
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self.i += 1
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terminated = False
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truncated = self.i >= 15 + self.start_at_t
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return self._get_obs(), float(self.i % 3), terminated, truncated, {}
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def _get_obs(self):
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return np.array([self.i], dtype=np.float32)
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class MultiAgentDebugCounterEnv(MultiAgentEnv):
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def __init__(self, config):
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super().__init__()
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self.num_agents = config["num_agents"]
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self.base_episode_len = config.get("base_episode_len", 103)
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# Observation dims:
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# 0=agent ID.
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# 1=episode ID (0.0 for obs after reset).
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# 2=env ID (0.0 for obs after reset).
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# 3=ts (of the agent).
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self.observation_space = gym.spaces.Dict(
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{
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aid: gym.spaces.Box(float("-inf"), float("inf"), (4,))
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for aid in range(self.num_agents)
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}
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)
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# Actions are always:
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# (episodeID, envID) as floats.
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self.action_space = gym.spaces.Dict(
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{
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aid: gym.spaces.Box(-float("inf"), float("inf"), shape=(2,))
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for aid in range(self.num_agents)
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}
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)
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self.timesteps = [0] * self.num_agents
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self.terminateds = set()
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self.truncateds = set()
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def reset(self, *, seed=None, options=None):
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self.timesteps = [0] * self.num_agents
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self.terminateds = set()
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self.truncateds = set()
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return {
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i: np.array([i, 0.0, 0.0, 0.0], dtype=np.float32)
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for i in range(self.num_agents)
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}, {}
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def step(self, action_dict):
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obs, rew, terminated, truncated = {}, {}, {}, {}
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for i, action in action_dict.items():
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self.timesteps[i] += 1
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obs[i] = np.array([i, action[0], action[1], self.timesteps[i]])
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rew[i] = self.timesteps[i] % 3
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terminated[i] = False
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truncated[i] = (
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True if self.timesteps[i] > self.base_episode_len + i else False
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)
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if terminated[i]:
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self.terminateds.add(i)
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if truncated[i]:
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self.truncateds.add(i)
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terminated["__all__"] = len(self.terminateds) == self.num_agents
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truncated["__all__"] = len(self.truncateds) == self.num_agents
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return obs, rew, terminated, truncated, {}
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