Module Fehu_envs.CartpoleSource

Example

Train an agent to balance the pole:

  let rng = Rune.Rng.create () in
  let env = Fehu_envs.Cartpole.make ~rng () in
  let obs, _ = Fehu.Env.reset env () in
  let rec run_episode total_reward =
    let action = (* DQN or policy gradient decision *) in
    let t = Fehu.Env.step env action in
    let new_total = total_reward +. t.reward in
    if t.terminated || t.truncated then
      Printf.printf "Episode reward: %.0f\n" new_total
    else
      run_episode new_total
  in
  run_episode 0.0

Tips

• One of the most popular RL benchmarks, considered solved at 475/500 average reward
• Good for testing DQN, REINFORCE, A2C, and PPO algorithms
• Requires learning to balance competing objectives (position and angle)
• Observation space is continuous, making it ideal for neural network policies