mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
import gym
|
|
import numpy as np
|
|
|
|
from ray.rllib.env.multi_agent_env import MultiAgentEnv
|
|
|
|
|
|
class DebugCounterEnv(gym.Env):
|
|
"""Simple Env that yields a ts counter as observation (0-based).
|
|
|
|
Actions have no effect.
|
|
The episode length is always 15.
|
|
Reward is always: current ts % 3.
|
|
"""
|
|
|
|
def __init__(self, config=None):
|
|
config = config or {}
|
|
self.action_space = gym.spaces.Discrete(2)
|
|
self.observation_space = gym.spaces.Box(0, 100, (1,), dtype=np.float32)
|
|
self.start_at_t = int(config.get("start_at_t", 0))
|
|
self.i = self.start_at_t
|
|
|
|
def reset(self):
|
|
self.i = self.start_at_t
|
|
return self._get_obs()
|
|
|
|
def step(self, action):
|
|
self.i += 1
|
|
return self._get_obs(), float(self.i % 3), self.i >= 15 + self.start_at_t, {}
|
|
|
|
def _get_obs(self):
|
|
return np.array([self.i], dtype=np.float32)
|
|
|
|
|
|
class MultiAgentDebugCounterEnv(MultiAgentEnv):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self._skip_env_checking = True
|
|
self.num_agents = config["num_agents"]
|
|
self.base_episode_len = config.get("base_episode_len", 103)
|
|
# Actions are always:
|
|
# (episodeID, envID) as floats.
|
|
self.action_space = gym.spaces.Box(-float("inf"), float("inf"), shape=(2,))
|
|
# Observation dims:
|
|
# 0=agent ID.
|
|
# 1=episode ID (0.0 for obs after reset).
|
|
# 2=env ID (0.0 for obs after reset).
|
|
# 3=ts (of the agent).
|
|
self.observation_space = gym.spaces.Box(float("-inf"), float("inf"), (4,))
|
|
self.timesteps = [0] * self.num_agents
|
|
self.dones = set()
|
|
|
|
def reset(self):
|
|
self.timesteps = [0] * self.num_agents
|
|
self.dones = set()
|
|
return {
|
|
i: np.array([i, 0.0, 0.0, 0.0], dtype=np.float32)
|
|
for i in range(self.num_agents)
|
|
}
|
|
|
|
def step(self, action_dict):
|
|
obs, rew, done = {}, {}, {}
|
|
for i, action in action_dict.items():
|
|
self.timesteps[i] += 1
|
|
obs[i] = np.array([i, action[0], action[1], self.timesteps[i]])
|
|
rew[i] = self.timesteps[i] % 3
|
|
done[i] = True if self.timesteps[i] > self.base_episode_len + i else False
|
|
if done[i]:
|
|
self.dones.add(i)
|
|
done["__all__"] = len(self.dones) == self.num_agents
|
|
return obs, rew, done, {}
|