halfcheetah_cql:
    env: 
        grid_search:
            #- ray.rllib.examples.env.d4rl_env.halfcheetah_random
            #- ray.rllib.examples.env.d4rl_env.halfcheetah_medium
            - ray.rllib.examples.env.d4rl_env.halfcheetah_expert
            #- ray.rllib.examples.env.d4rl_env.halfcheetah_medium_replay
    run: CQL
    config:
        # SAC Configs
        #input: d4rl.halfcheetah-random-v0
        #input: d4rl.halfcheetah-medium-v0
        input: d4rl.halfcheetah-expert-v0
        #input: d4rl.halfcheetah-medium-replay-v0

        # Works for both torch and tf.
        framework: tf
        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

        evaluation_interval: 3
        evaluation_config:
            input: sampler