class_docstr: 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.
class_docstr_tok: ["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","."]
code: 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
  
file_path: ray/rllib/examples/env/debug_counter_env.py
imports: import gymnasium
  import numpy
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
label_desc: Use image node_id 1 to create a new DebugCounterEnv object from inherited base classes: gym with example: obj = DebugCounterEnv(config)