text: 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.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, {}
  