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| from easydict import EasyDict | |
| lunarlander_dqfd_config = dict( | |
| exp_name='lunarlander_dqfd_seed0', | |
| env=dict( | |
| # Whether to use shared memory. Only effective if "env_manager_type" is 'subprocess' | |
| collector_env_num=8, | |
| evaluator_env_num=8, | |
| env_id='LunarLander-v2', | |
| n_evaluator_episode=8, | |
| stop_value=200, | |
| ), | |
| policy=dict( | |
| cuda=True, | |
| model=dict( | |
| obs_shape=8, | |
| action_shape=4, | |
| encoder_hidden_size_list=[512, 64], | |
| dueling=True, | |
| ), | |
| nstep=3, | |
| discount_factor=0.97, | |
| learn=dict( | |
| batch_size=64, | |
| learning_rate=0.001, | |
| lambda1=1.0, | |
| lambda2=1.0, | |
| lambda3=1e-5, | |
| per_train_iter_k=10, | |
| expert_replay_buffer_size=10000, # justify the buffer size of the expert buffer | |
| ), | |
| collect=dict( | |
| n_sample=64, | |
| # Users should add their own model path here. Model path should lead to a model. | |
| # Absolute path is recommended. | |
| # In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``. | |
| model_path='model_path_placeholder', | |
| # Cut trajectories into pieces with length "unroll_len". | |
| unroll_len=1, | |
| ), | |
| eval=dict(evaluator=dict(eval_freq=50, )), # note: this is the times after which you learns to evaluate | |
| other=dict( | |
| eps=dict( | |
| type='exp', | |
| start=0.95, | |
| end=0.1, | |
| decay=10000, | |
| ), | |
| replay_buffer=dict(replay_buffer_size=20000, ), | |
| ), | |
| ), | |
| ) | |
| lunarlander_dqfd_config = EasyDict(lunarlander_dqfd_config) | |
| main_config = lunarlander_dqfd_config | |
| lunarlander_dqfd_create_config = dict( | |
| env=dict( | |
| type='lunarlander', | |
| import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'], | |
| ), | |
| env_manager=dict(type='subprocess'), | |
| policy=dict(type='dqfd'), | |
| ) | |
| lunarlander_dqfd_create_config = EasyDict(lunarlander_dqfd_create_config) | |
| create_config = lunarlander_dqfd_create_config | |
| if __name__ == '__main__': | |
| # or you can enter `ding -m serial_dqfd -c lunarlander_dqfd_config.py -s 0` | |
| # then input ``lunarlander_dqfd_config.py`` upon the instructions. | |
| # The reason we need to input the dqfd config is we have to borrow its ``_get_train_sample`` function | |
| # in the collector part even though the expert model may be generated from other Q learning algos. | |
| from ding.entry.serial_entry_dqfd import serial_pipeline_dqfd | |
| from dizoo.box2d.lunarlander.config import lunarlander_dqfd_config, lunarlander_dqfd_create_config | |
| expert_main_config = lunarlander_dqfd_config | |
| expert_create_config = lunarlander_dqfd_create_config | |
| serial_pipeline_dqfd([main_config, create_config], [expert_main_config, expert_create_config], seed=0) | |