from easydict import EasyDict

pong_impala_config = dict(
    exp_name='impala_log/pong_impala_seed0',
    env=dict(
        collector_env_num=12,
        evaluator_env_num=8,
        n_evaluator_episode=8,
        stop_value=21,
        env_id='PongNoFrameskip-v4',
        #'ALE/Pong-v5' is available. But special setting is needed after gym make.
        frame_stack=4,
    ),
    policy=dict(
        cuda=True,
        priority=False,
        # (int) the trajectory length to calculate v-trace target
        unroll_len=64,
        model=dict(
            obs_shape=[4, 84, 84],
            action_shape=6,
            encoder_hidden_size_list=[64, 128, 256],
            critic_head_hidden_size=256,
            critic_head_layer_num=2,
            actor_head_hidden_size=256,
            actor_head_layer_num=2,
            # impala_cnn_encoder=True,
        ),
        learn=dict(
            # (int) collect n_sample data, train model update_per_collect times
            # here we follow impala serial pipeline
            update_per_collect=2,
            # (int) the number of data for a train iteration
            batch_size=128,
            # optim_type='rmsprop',
            grad_clip_type='clip_norm',
            clip_value=0.5,
            learning_rate=0.0006,
            # (float) loss weight of the value network, the weight of policy network is set to 1
            value_weight=0.5,
            # (float) loss weight of the entropy regularization, the weight of policy network is set to 1
            entropy_weight=0.01,
            # (float) discount factor for future reward, defaults int [0, 1]
            discount_factor=0.99,
            # (float) additional discounting parameter
            lambda_=0.95,
            # (float) clip ratio of importance weights
            rho_clip_ratio=1.0,
            # (float) clip ratio of importance weights
            c_clip_ratio=1.0,
            # (float) clip ratio of importance sampling
            rho_pg_clip_ratio=1.0,
        ),
        collect=dict(
            # (int) collect n_sample data, train model n_iteration times
            n_sample=16,
            collector=dict(collect_print_freq=1000, ),
        ),
        eval=dict(evaluator=dict(eval_freq=2000, )),
        other=dict(replay_buffer=dict(replay_buffer_size=10000, sliced=False), ),
    ),
)
main_config = EasyDict(pong_impala_config)

pong_impala_create_config = dict(
    env=dict(
        type='atari',
        import_names=['dizoo.atari.envs.atari_env'],
    ),
    env_manager=dict(type='subprocess'),
    policy=dict(type='impala'),
)
create_config = EasyDict(pong_impala_create_config)

if __name__ == '__main__':
    # or you can enter `ding -m serial -c pong_impala_config.py -s 0`
    from ding.entry import serial_pipeline
    serial_pipeline((main_config, create_config), seed=0)