code: def rcv_action_send_exp(conn, game: str):
      """Run the remote agents.
  
      Receive action from the main learner, perform one step of simulation and
      send back collected experience.
      """
      env = env_utils.create_env(game, clip_rewards=True)
      while True:
          obs = env.reset()
          done = False
          # Observations fetched from Atari env need additional batch dimension.
          state = obs[None, ...]
          while not done:
              conn.send(state)
              action = conn.recv()
              obs, reward, done, _ = env.step(action)
              next_state = obs[None, ...] if not done else None
              experience = (state, action, reward, done)
              conn.send(experience)
              if done:
                  break
              state = next_state
  
file_path: flax/examples/ppo/agent.py
imports: import collections
  import functools
  import multiprocessing
  from typing import Any, Callable
  import flax
  import jax
  import numpy
  import env_utils
label_desc: Use image node_id 2 for calling a global function with example usage: rcv_action_send_exp(conn, game) without return types