class_docstr: Algo that produces random actions and never learns.
class_docstr_tok: ["Algo","that","produces","random","actions","and","never","learns","."]
code: def step(self):
      rewards = []
      steps = 0
      for _ in range(self.config.rollouts_per_iteration):
          self.env.reset()
          terminated = truncated = False
          reward = 0.0
          while not terminated and not truncated:
              action = self.env.action_space.sample()
              _, rew, terminated, truncated, _ = self.env.step(action)
              reward += rew
              steps += 1
          rewards.append(reward)
      return {
          "episode_reward_mean": np.mean(rewards),
          "timesteps_this_iter": steps,
      }
  
file_path: ray/rllib/algorithms/random_agent/random_agent.py
imports: import numpy
  from typing import Optional
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import Deprecated, ALGO_DEPRECATION_WARNING
label_desc: Use image node_id 3 for calling the RandomAgent obj's underlying member method code with example usage: obj.step() and returns: dict