# @OldAPIStack
# Run e.g. on a g3.16xlarge (4 GPUs) with `num_gpus=1` (1 for each trial;
# MsPacman torch + tf; Pong torch + tf).
# Uses the hyperparameters published in [2] (see rllib/algorithms/sac/README.md).
atari-sac-tf-and-torch:
    env:
        grid_search:
            - ale_py:ALE/MsPacman-v5
            - ale_py:ALE/Pong-v5
    run: SAC
    stop:
        timesteps_total: 20000000
    config:
        # Works for both torch and tf.
        framework:
            grid_search: [tf, torch]
        env_config:
            frameskip: 1  # no frameskip
        gamma: 0.99
        q_model_config:
            hidden_activation: relu
            hidden_layer_sizes: [512]
        policy_model_config:
            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
        # auto = 0.98 * -log(1/|A|)
        target_entropy: auto
        clip_rewards: 1.0
        n_step: 1
        rollout_fragment_length: 1
        replay_buffer_config:
          type: MultiAgentPrioritizedReplayBuffer
          capacity: 1000000
          # How many steps of the model to sample before learning starts.
          # If True prioritized replay buffer will be used.
          prioritized_replay_alpha: 0.6
          prioritized_replay_beta: 0.4
          prioritized_replay_eps: 1e-6
        num_steps_sampled_before_learning_starts: 100000
        train_batch_size: 64
        min_sample_timesteps_per_iteration: 4
        # Paper uses 20k random timesteps, which is not exactly the same, but
        # seems to work nevertheless. We use 100k here for the longer Atari
        # runs (DQN style: filling up the buffer a bit before learning).
        optimization:
            actor_learning_rate: 0.0003
            critic_learning_rate: 0.0003
            entropy_learning_rate: 0.0003
        num_env_runners: 0
        num_gpus: 1
        metrics_num_episodes_for_smoothing: 5