Ray Tune搜索算法中能否指定episodes_this_iter参数？新手配置遇阻

Hey there! Let's clear up this confusion since you're just getting started with Ray and Ray Tune.

First, let's get to the core of your question: episodes_this_iter is not a parameter you set directly in tune.run()—it's a metric that Ray Tune automatically tracks during training. That's why your code throws an error when you include it as a top-level argument to tune.run(); remove it, and everything works because you're no longer passing an unexpected parameter.

To answer your main question: Yes, episodes_this_iter (and similar auto-filled fields like steps_this_iter) are intended to be used as part of stop conditions, or as metrics for schedulers/search algorithms to make decisions. They're read-only indicators of how your training is progressing each iteration, not settings you can pre-define.

What if you want to control how many episodes run per iteration? #

If your goal is to limit each training iteration to 2500 episodes, you need to use configuration parameters specific to the PPOTrainer you're using. For PPO, this is typically handled via parameters like timesteps_per_iteration (controls the total timesteps per iteration) or adjusting train_batch_size and sgd_minibatch_size to indirectly cap episode counts. You'd add these to the config dictionary, not as a top-level tune.run() argument.

Fixed Code Example #

Here's how your code should look (with notes on valid uses of episodes_this_iter):

ray.init()
tune.run(
    PPOTrainer,
    stop={
        "training_iteration": 1000,
        # ✅ Valid use: Use episodes_this_iter as a stop condition
        # "episodes_this_iter": 2500  # Uncomment this if you want to stop when an iteration hits 2500 episodes
    },
    config={
        "env": qsdm.QSDEnv,
        "env_config": defaultconfig,
        "num_gpus": 0,
        "num_workers": 1,
        "lr": tune.grid_search([0.00005, 0.00001, 0.0001]),
        # ✅ To control iteration length, use PPOTrainer-specific params
        # "timesteps_per_iteration": 2500  # Adjust based on your episode length to hit ~2500 episodes per iter
    }
)

Key Takeaway #

• Auto-filled fields like episodes_this_iter are metrics Ray collects for you—you can't set their values directly.
• Use them in the stop dictionary to end training when a certain episode count per iteration is reached.
• To define how much training happens per iteration, use the configuration parameters of your chosen Trainer (like PPOTrainer's timesteps_per_iteration).

内容的提问来源于stack exchange，提问作者sbrand