8b3f79f3079834f00c7ec62d5decd6bc026c1bc8,python/ray/tune/examples/pbt_ppo_example.py,,,#,18

Before Change

        custom_explore_fn=explore)

    ray.init()
    run(
        "PPO",
        name="pbt_humanoid_test",
        scheduler=pbt,
        num_samples=8,
        config={
            "env": "Humanoid-v1",
            "kl_coeff": 1.0,
            "num_workers": 8,
            "num_gpus": 1,
            "model": {
                "free_log_std": True
            },
            // These params are tuned from a fixed starting value.
            "lambda": 0.95,
            "clip_param": 0.2,
            "lr": 1e-4,
            // These params start off randomly drawn from a set.
            "num_sgd_iter": sample_from(
                lambda spec: random.choice([10, 20, 30])),
            "sgd_minibatch_size": sample_from(
                lambda spec: random.choice([128, 512, 2048])),
            "train_batch_size": sample_from(
                lambda spec: random.choice([10000, 20000, 40000]))
        })

After Change

        },
        custom_explore_fn=explore)

    analysis = tune.run(
        "PPO",
        name="pbt_humanoid_test",
        scheduler=pbt,
        num_samples=8,
        metric="episode_reward_mean",
        mode="max",
        config={
            "env": "Humanoid-v1",
            "kl_coeff": 1.0,
            "num_workers": 8,
            "num_gpus": 1,
            "model": {
                "free_log_std": True
            },
            // These params are tuned from a fixed starting value.
            "lambda": 0.95,
            "clip_param": 0.2,
            "lr": 1e-4,
            // These params start off randomly drawn from a set.
            "num_sgd_iter": tune.choice([10, 20, 30]),
            "sgd_minibatch_size": tune.choice([128, 512, 2048]),
            "train_batch_size": tune.choice([10000, 20000, 40000])
        })

    print("best hyperparameters: ", analysis.best_config)

Instances