# Our implementation of SAC discrete can reach up
# to ~750 reward in 40k timesteps. Run e.g. on a g3.4xlarge with `num_gpus=1`.
# Uses the hyperparameters published in [2] (see rllib/agents/sac/README.md).
mspacman-sac-tf:
    env: MsPacmanNoFrameskip-v4
    run: SAC
    stop:
        episode_reward_mean: 800
        timesteps_total: 100000
    config:
        use_pytorch: false
        gamma: 0.99
        # state-preprocessor=Our default Atari Conv2D-net.
        use_state_preprocessor: true
        Q_model:
            hidden_activation: relu
            hidden_layer_sizes: [512]
        policy_model:
            hidden_activation: relu
            hidden_layer_sizes: [512]
        # Do hard syncs.
        # Soft-syncs seem to work less reliably for discrete action spaces.
        tau: 1.0
        target_network_update_freq: 8000
        # paper uses: 0.98 * -log(1/|A|)
        target_entropy: 1.755
        clip_rewards: 1.0
        no_done_at_end: False
        n_step: 1
        rollout_fragment_length: 1
        prioritized_replay: true
        train_batch_size: 64
        timesteps_per_iteration: 4
        # Paper uses 20k random timesteps, which is not exactly the same, but
        # seems to work nevertheless.
        learning_starts: 20000
        optimization:
            actor_learning_rate: 0.0003
            critic_learning_rate: 0.0003
            entropy_learning_rate: 0.0003
        num_workers: 0
        num_gpus: 0
        metrics_smoothing_episodes: 5