from easydict import EasyDict

collector_env_num = 8
evaluator_env_num = 8
nstep = 5
max_env_step = int(10e6)

montezuma_ngu_config = dict(
    exp_name='montezuma_ngu_seed0',
    env=dict(
        collector_env_num=collector_env_num,
        evaluator_env_num=evaluator_env_num,
        n_evaluator_episode=8,
        env_id='MontezumaRevengeNoFrameskip-v4',
        #'ALE/MontezumaRevenge-v5' is available. But special setting is needed after gym make.
        obs_plus_prev_action_reward=True,  # use specific env wrapper for ngu policy
        stop_value=int(1e5),
        frame_stack=4,
    ),
    rnd_reward_model=dict(
        intrinsic_reward_type='add',
        learning_rate=0.001,
        obs_shape=[4, 84, 84],
        action_shape=18,
        batch_size=320,
        update_per_collect=10,
        only_use_last_five_frames_for_icm_rnd=False,
        clear_buffer_per_iters=10,
        nstep=nstep,
        hidden_size_list=[128, 128, 64],
        type='rnd-ngu',
    ),
    episodic_reward_model=dict(
        # means if using rescale trick to the last non-zero reward
        # when combing extrinsic and intrinsic reward.
        # the rescale trick only used in:
        # 1. sparse reward env minigrid, in which the last non-zero reward is a strong positive signal
        # 2. the last reward of each episode directly reflects the agent's completion of the task, e.g. lunarlander
        # Note that the ngu intrinsic reward is a positive value (max value is 5), in these envs,
        # the last non-zero reward should not be overwhelmed by intrinsic rewards, so we need rescale the
        # original last nonzero extrinsic reward.
        # please refer to ngu_reward_model for details.
        last_nonzero_reward_rescale=False,
        # means the rescale value for the last non-zero reward, only used when last_nonzero_reward_rescale is True
        # please refer to ngu_reward_model for details.
        last_nonzero_reward_weight=1,
        intrinsic_reward_type='add',
        learning_rate=0.001,
        obs_shape=[4, 84, 84],
        action_shape=18,
        batch_size=320,
        update_per_collect=10,  # 32*100/64=50
        only_use_last_five_frames_for_icm_rnd=False,
        clear_buffer_per_iters=10,
        nstep=nstep,
        hidden_size_list=[128, 128, 64],
        type='episodic',
    ),
    policy=dict(
        cuda=True,
        on_policy=False,
        priority=True,
        priority_IS_weight=True,
        discount_factor=0.997,
        nstep=nstep,
        burnin_step=20,
        # (int) <learn_unroll_len> is the total length of [sequence sample] minus
        # the length of burnin part in [sequence sample],
        # i.e., <sequence sample length> = <unroll_len> = <burnin_step> + <learn_unroll_len>
        learn_unroll_len=80,  # set this key according to the episode length
        model=dict(
            obs_shape=[4, 84, 84],
            action_shape=18,
            encoder_hidden_size_list=[128, 128, 512],
            collector_env_num=collector_env_num,
        ),
        learn=dict(
            update_per_collect=8,
            batch_size=64,
            learning_rate=0.0005,
            target_update_theta=0.001,
        ),
        collect=dict(
            # NOTE: It is important that set key traj_len_inf=True here,
            # to make sure self._traj_len=INF in serial_sample_collector.py.
            # In sequence-based policy, for each collect_env,
            # we want to collect data of length self._traj_len=INF
            # unless the episode enters the 'done' state.
            # In each collect phase, we collect a total of <n_sample> sequence samples.
            n_sample=32,
            traj_len_inf=True,
            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=int(2e3),
                # (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,
            )
        ),
    ),
)
montezuma_ngu_config = EasyDict(montezuma_ngu_config)
main_config = montezuma_ngu_config
montezuma_ngu_create_config = dict(
    env=dict(
        type='atari',
        import_names=['dizoo.atari.envs.atari_env'],
    ),
    env_manager=dict(type='subprocess'),
    policy=dict(type='ngu'),
    rnd_reward_model=dict(type='rnd-ngu'),
    episodic_reward_model=dict(type='episodic'),
)
montezuma_ngu_create_config = EasyDict(montezuma_ngu_create_config)
create_config = montezuma_ngu_create_config

if __name__ == "__main__":
    from ding.entry import serial_pipeline_reward_model_ngu
    serial_pipeline_reward_model_ngu([main_config, create_config], seed=0, max_env_step=max_env_step)