from easydict import EasyDict
from ding.entry import serial_pipeline

collector_env_num = 8
evaluator_env_num = 5
pong_r2d2_config = dict(
    exp_name='debug_pong_r2d2_n5_bs2_ul40',
    env=dict(
        collector_env_num=collector_env_num,
        evaluator_env_num=evaluator_env_num,
        n_evaluator_episode=5,
        stop_value=20,
        env_id='PongNoFrameskip-v4',
        frame_stack=4,
    ),
    policy=dict(
        cuda=True,
        on_policy=False,
        priority=True,
        priority_IS_weight=True,
        model=dict(
            obs_shape=[4, 84, 84],
            action_shape=6,
            encoder_hidden_size_list=[128, 128, 512],
        ),
        discount_factor=0.997,
        burnin_step=2,
        nstep=5,
        # (int) the whole sequence length to unroll the RNN network minus
        # the timesteps of burnin part,
        # i.e., <the whole sequence length> = <burnin_step> + <unroll_len>
        unroll_len=40,
        learn=dict(
            # according to the R2D2 paper, actor parameter update interval is 400
            # environment timesteps, and in per collect phase, we collect 32 sequence
            # samples, the length of each samlpe sequence is <burnin_step> + <unroll_len>,
            # which is 100 in our seeting, 32*100/400=8, so we set update_per_collect=8
            # in most environments
            update_per_collect=8,
            batch_size=64,  # TODO(pu)
            learning_rate=0.0005,
            target_update_theta=0.001,
        ),
        collect=dict(
            # NOTE it is important that don't include key n_sample here, to make sure self._traj_len=INF
            each_iter_n_sample=32,
            env_num=collector_env_num,
        ),
        eval=dict(env_num=evaluator_env_num, ),
        other=dict(
            eps=dict(
                type='exp',
                start=0.95,
                end=0.05,
                decay=1e5,
            ),
            replay_buffer=dict(
                replay_buffer_size=20000,  # TODO(pu)
                # (Float type) How much prioritization is used: 0 means no prioritization while 1 means full prioritization
                alpha=0.6,
                # (Float type)  How much correction is used: 0 means no correction while 1 means full correction
                beta=0.4,
            )
        ),
    ),
)
pong_r2d2_config = EasyDict(pong_r2d2_config)
main_config = pong_r2d2_config
pong_r2d2_create_config = dict(
    env=dict(
        type='atari',
        import_names=['dizoo.atari.envs.atari_env'],
    ),
    env_manager=dict(type='base'),
    policy=dict(type='r2d2'),
)
pong_r2d2_create_config = EasyDict(pong_r2d2_create_config)
create_config = pong_r2d2_create_config

if __name__ == "__main__":
    serial_pipeline([main_config, create_config], seed=0)
