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