apex-breakoutnoframeskip-v4:
    env: BreakoutNoFrameskip-v4
    run: APEX
    frameworks: [ "tf", "tf2", "torch" ]
    stop:
        time_total_s: 3600
    config:
        double_q: false
        dueling: false
        num_atoms: 1
        noisy: false
        n_step: 3
        lr: .0001
        adam_epsilon: .00015
        hiddens: [512]
        buffer_size: 1000000
        exploration_config:
            epsilon_timesteps: 200000
            final_epsilon: 0.01
        prioritized_replay_alpha: 0.5
        final_prioritized_replay_beta: 1.0
        prioritized_replay_beta_annealing_timesteps: 2000000
        num_gpus: 1
        num_workers: 8
        num_envs_per_worker: 8
        rollout_fragment_length: 20
        train_batch_size: 512
        target_network_update_freq: 50000
        timesteps_per_iteration: 25000

appo-pongnoframeskip-v4:
    env: PongNoFrameskip-v4
    run: APPO
    frameworks: [ "tf", "tf2", "torch" ]
    stop:
        time_total_s: 2000
    config:
        vtrace: True
        use_kl_loss: False
        rollout_fragment_length: 50
        train_batch_size: 750
        num_workers: 31
        broadcast_interval: 1
        max_sample_requests_in_flight_per_worker: 1
        num_multi_gpu_tower_stacks: 1
        num_envs_per_worker: 8
        num_sgd_iter: 2
        vf_loss_coeff: 1.0
        clip_param: 0.3
        num_gpus: 1
        grad_clip: 10
        model:
            dim: 42

# Bring cql test back after we make sure it learns.
#cql-halfcheetahbulletenv-v0:
#    env: HalfCheetahBulletEnv-v0
#    run: CQL
#    frameworks: [ "tf", "tf2", "torch" ]
#    stop:
#        time_total_s: 1800
#    config:
#        # Use input produced by expert SAC algo.
#        input: ["~/halfcheetah_expert_sac.zip"]
#        actions_in_input_normalized: true
#
#        soft_horizon: False
#        horizon: 1000
#        Q_model:
#            fcnet_activation: relu
#            fcnet_hiddens: [256, 256, 256]
#        policy_model:
#            fcnet_activation: relu
#            fcnet_hiddens: [256, 256, 256]
#        tau: 0.005
#        target_entropy: auto
#        no_done_at_end: false
#        n_step: 3
#        rollout_fragment_length: 1
#        prioritized_replay: false
#        train_batch_size: 256
#        target_network_update_freq: 0
#        timesteps_per_iteration: 1000
#        learning_starts: 256
#        optimization:
#            actor_learning_rate: 0.0001
#            critic_learning_rate: 0.0003
#            entropy_learning_rate: 0.0001
#        num_workers: 0
#        num_gpus: 1
#        metrics_smoothing_episodes: 5
#
#        # CQL Configs
#        min_q_weight: 5.0
#        bc_iters: 20000
#        temperature: 1.0
#        num_actions: 10
#        lagrangian: False
#
#        # Switch on online evaluation.
#        evaluation_interval: 3
#        evaluation_config:
#            input: sampler

sac-halfcheetahbulletenv-v0:
    env: HalfCheetahBulletEnv-v0
    run: SAC
    frameworks: [ "tf", "tf2", "torch" ]
    stop:
        time_total_s: 3600
    config:
        horizon: 1000
        soft_horizon: false
        Q_model:
            fcnet_activation: relu
            fcnet_hiddens: [256, 256]
        policy_model:
            fcnet_activation: relu
            fcnet_hiddens: [256, 256]
        tau: 0.005
        target_entropy: auto
        no_done_at_end: false
        n_step: 3
        rollout_fragment_length: 1
        prioritized_replay: true
        train_batch_size: 256
        target_network_update_freq: 1
        timesteps_per_iteration: 1000
        learning_starts: 10000
        optimization:
            actor_learning_rate: 0.0003
            critic_learning_rate: 0.0003
            entropy_learning_rate: 0.0003
        num_workers: 0
        num_gpus: 1
        metrics_smoothing_episodes: 5