Tod Rla Walkthrough May 2026

TOD RLA (Teacher-Of-Days Reinforcement Learning from Algorithms) — assumed here to mean a timed, curriculum-style RLA approach for training agents. This walkthrough covers objectives, environment setup, reward design, training loop, debugging, and evaluation. I assume you want a complete, practical guide to implement and run an RLA pipeline; adjust specifics to your framework (PyTorch, JAX, TF) and environment.

for epoch in range(EPOCHS):
  for _ in range(episodes_per_epoch):
    obs = env.reset()
    done = False
    while not done:
      action = agent.act(obs)
      next_obs, reward, done, info = env.step(action)
      replay.push(obs, action, reward, next_obs, done, level=curr_level)
      obs = next_obs
      if off_policy and replay.size() > batch_size:
        agent.update(replay.sample(batch_size))
  eval_metrics = evaluate(agent, val_seeds, level=curr_level)
  curriculum.update(eval_metrics)
  logger.save_checkpoint(agent, curriculum)

The foundation of the walkthrough relies on high-fidelity digital capture. tod rla walkthrough