""" Overview: Here is the behaviour cloning (BC) default config for gfootball. For main entry, please refer to the gfootball_bc_rule_main.py, gfootball_bc_rule_lt0_main.py, gfootball_bc_kaggle5th_main.py in the same directory. """ from easydict import EasyDict collector_env_num = 8 evaluator_env_num = 5 gfootball_bc_config = dict( exp_name='gfootball_bc_seed0', env=dict( collector_env_num=collector_env_num, evaluator_env_num=evaluator_env_num, n_evaluator_episode=evaluator_env_num, stop_value=999, # Don't stop until training epochs env_name="11_vs_11_easy_stochastic", # env_name="11_vs_11_stochastic", # default: medium # env_name="11_vs_11_hard_stochastic", save_replay_gif=False, manager=dict(shared_memory=False, ), ), policy=dict( env_name='gfootball', continuous=False, # action_shape is effective only when continuous=False action_shape=19, show_train_test_accuracy=False, # Note, only if show_train_test_accuracy=True, we will test accuracy in train dataset and validation dataset # use the pre-trained BC model in the path . # Users should add their own BC 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``. bc_model_path='bc_model_path_placeholder', cuda=True, model=dict(), learn=dict( update_per_collect=20, batch_size=512, learning_rate=0.0001, target_update_freq=500, weight_decay=None, ce_label_smooth=False, show_accuracy=False, ), collect=dict(n_sample=4096, ), eval=dict(evaluator=dict(eval_freq=1000)), other=dict( eps=dict( type='exp', start=0.95, end=0.1, decay=10000, ), replay_buffer=dict(replay_buffer_size=int(1e6), ), ), ), ) gfootball_bc_config = EasyDict(gfootball_bc_config) main_config = gfootball_bc_config gfootball_bc_create_config = dict( env=dict( type='gfootball', import_names=['dizoo.gfootball.envs.gfootball_env'], ), env_manager=dict(type='subprocess'), policy=dict(type='bc'), ) gfootball_bc_create_config = EasyDict(gfootball_bc_create_config) create_config = gfootball_bc_create_config