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from easydict import EasyDict |
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from torch.nn.modules.activation import Threshold |
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league_demo_ppo_config = dict( |
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exp_name="league_demo_ppo", |
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env=dict( |
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collector_env_num=8, |
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evaluator_env_num=10, |
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n_evaluator_episode=100, |
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env_type='prisoner_dilemma', |
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stop_value=[-10.1, -5.05], |
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), |
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policy=dict( |
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cuda=False, |
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action_space='discrete', |
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model=dict( |
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obs_shape=2, |
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action_shape=2, |
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action_space='discrete', |
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encoder_hidden_size_list=[32, 32], |
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critic_head_hidden_size=32, |
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actor_head_hidden_size=32, |
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share_encoder=False, |
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), |
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learn=dict( |
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update_per_collect=3, |
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batch_size=32, |
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learning_rate=0.00001, |
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entropy_weight=0.0, |
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learner=dict(log_policy=False), |
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), |
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collect=dict( |
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n_episode=128, unroll_len=1, discount_factor=1.0, gae_lambda=1.0, collector=dict(get_train_sample=True, ) |
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), |
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other=dict( |
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league=dict( |
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player_category=['default'], |
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path_policy="league_demo_ppo/policy", |
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active_players=dict( |
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main_player=1, |
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main_exploiter=1, |
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league_exploiter=1, |
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), |
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main_player=dict( |
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one_phase_step=200, |
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branch_probs=dict( |
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pfsp=0.5, |
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sp=0.5, |
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), |
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strong_win_rate=0.7, |
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), |
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main_exploiter=dict( |
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one_phase_step=200, |
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branch_probs=dict(main_players=1.0, ), |
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strong_win_rate=0.7, |
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min_valid_win_rate=0.3, |
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), |
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league_exploiter=dict( |
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one_phase_step=200, |
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branch_probs=dict(pfsp=1.0, ), |
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strong_win_rate=0.7, |
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mutate_prob=0.5, |
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), |
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use_pretrain=False, |
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use_pretrain_init_historical=False, |
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payoff=dict( |
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type='battle', |
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decay=0.99, |
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min_win_rate_games=8, |
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), |
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metric=dict( |
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mu=0, |
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sigma=25 / 3, |
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beta=25 / 3 / 2, |
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tau=0.0, |
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draw_probability=0.02, |
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), |
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), |
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), |
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), |
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) |
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league_demo_ppo_config = EasyDict(league_demo_ppo_config) |
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