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feature(xcy): add muzero config for connect4 (#107)
* polish(xcy):add muzero config for connect4 * polish(xcy):adjusting parameters in sp_mode
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zoo/board_games/connect4/config/connect4_muzero_bot_mode_config.py
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from easydict import EasyDict | ||
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# ============================================================== | ||
# begin of the most frequently changed config specified by the user | ||
# ============================================================== | ||
collector_env_num = 8 | ||
n_episode = 8 | ||
evaluator_env_num = 5 | ||
num_simulations = 50 | ||
update_per_collect = 50 | ||
reanalyze_ratio = 0. | ||
batch_size = 256 | ||
max_env_step = int(5e5) | ||
# ============================================================== | ||
# end of the most frequently changed config specified by the user | ||
# ============================================================== | ||
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||
connect4_muzero_config = dict( | ||
exp_name= | ||
f'data_mz_ctree/connect4_botmode_rulebot_seed0', | ||
env=dict( | ||
battle_mode='play_with_bot_mode', | ||
bot_action_type='rule', | ||
channel_last=True, | ||
collector_env_num=collector_env_num, | ||
evaluator_env_num=evaluator_env_num, | ||
n_evaluator_episode=evaluator_env_num, | ||
manager=dict(shared_memory=False, ), | ||
), | ||
policy=dict( | ||
model=dict( | ||
observation_shape=(3, 6, 7), | ||
action_space_size=7, | ||
image_channel=3, | ||
num_res_blocks=1, | ||
num_channels=64, | ||
support_scale=300, | ||
reward_support_size=601, | ||
value_support_size=601, | ||
), | ||
cuda=True, | ||
env_type='board_games', | ||
game_segment_length=int(6 * 7 / 2), # for battle_mode='play_with_bot_mode' | ||
update_per_collect=update_per_collect, | ||
batch_size=batch_size, | ||
optim_type='Adam', | ||
lr_piecewise_constant_decay=False, | ||
learning_rate=0.003, | ||
grad_clip_value=0.5, | ||
num_simulations=num_simulations, | ||
reanalyze_ratio=reanalyze_ratio, | ||
# NOTE:In board_games, we set large td_steps to make sure the value target is the final outcome. | ||
td_steps=int(6 * 7 / 2), # for battle_mode='play_with_bot_mode' | ||
# NOTE:In board_games, we set discount_factor=1. | ||
discount_factor=1, | ||
n_episode=n_episode, | ||
eval_freq=int(2e3), | ||
replay_buffer_size=int(1e5), | ||
collector_env_num=collector_env_num, | ||
evaluator_env_num=evaluator_env_num, | ||
), | ||
) | ||
connect4_muzero_config = EasyDict(connect4_muzero_config) | ||
main_config = connect4_muzero_config | ||
|
||
connect4_muzero_create_config = dict( | ||
env=dict( | ||
type='connect4', | ||
import_names=['zoo.board_games.connect4.envs.connect4_env'], | ||
), | ||
env_manager=dict(type='subprocess'), | ||
policy=dict( | ||
type='muzero', | ||
import_names=['lzero.policy.muzero'], | ||
), | ||
) | ||
connect4_muzero_create_config = EasyDict(connect4_muzero_create_config) | ||
create_config = connect4_muzero_create_config | ||
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if __name__ == "__main__": | ||
from lzero.entry import train_muzero | ||
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train_muzero([main_config, create_config], seed=1, max_env_step=max_env_step) |
83 changes: 83 additions & 0 deletions
83
zoo/board_games/connect4/config/connect4_muzero_sp_mode_config.py
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,83 @@ | ||
from easydict import EasyDict | ||
|
||
# ============================================================== | ||
# begin of the most frequently changed config specified by the user | ||
# ============================================================== | ||
collector_env_num = 8 | ||
n_episode = 8 | ||
evaluator_env_num = 5 | ||
num_simulations = 50 | ||
update_per_collect = 50 | ||
reanalyze_ratio = 0. | ||
batch_size = 256 | ||
max_env_step = int(5e5) | ||
# ============================================================== | ||
# end of the most frequently changed config specified by the user | ||
# ============================================================== | ||
|
||
connect4_muzero_config = dict( | ||
exp_name= | ||
f'data_mz_ctree/connect4_spmode_rulebot_seed0', | ||
env=dict( | ||
battle_mode='self_play_mode', | ||
bot_action_type='rule', | ||
channel_last=True, | ||
collector_env_num=collector_env_num, | ||
evaluator_env_num=evaluator_env_num, | ||
n_evaluator_episode=evaluator_env_num, | ||
manager=dict(shared_memory=False, ), | ||
), | ||
policy=dict( | ||
model=dict( | ||
observation_shape=(3, 6, 7), | ||
action_space_size=7, | ||
image_channel=3, | ||
num_res_blocks=1, | ||
num_channels=64, | ||
support_scale=300, | ||
reward_support_size=601, | ||
value_support_size=601, | ||
), | ||
cuda=True, | ||
env_type='board_games', | ||
game_segment_length=int(6 * 7), # for battle_mode='self_play_mode' | ||
update_per_collect=update_per_collect, | ||
batch_size=batch_size, | ||
optim_type='Adam', | ||
lr_piecewise_constant_decay=False, | ||
learning_rate=0.003, | ||
grad_clip_value=0.5, | ||
num_simulations=num_simulations, | ||
reanalyze_ratio=reanalyze_ratio, | ||
# NOTE:In board_games, we set large td_steps to make sure the value target is the final outcome. | ||
td_steps=int(6 * 7), # for battle_mode='self_play_mode' | ||
# NOTE:In board_games, we set discount_factor=1. | ||
discount_factor=1, | ||
n_episode=n_episode, | ||
eval_freq=int(2e3), | ||
replay_buffer_size=int(1e5), | ||
collector_env_num=collector_env_num, | ||
evaluator_env_num=evaluator_env_num, | ||
), | ||
) | ||
connect4_muzero_config = EasyDict(connect4_muzero_config) | ||
main_config = connect4_muzero_config | ||
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||
connect4_muzero_create_config = dict( | ||
env=dict( | ||
type='connect4', | ||
import_names=['zoo.board_games.connect4.envs.connect4_env'], | ||
), | ||
env_manager=dict(type='subprocess'), | ||
policy=dict( | ||
type='muzero', | ||
import_names=['lzero.policy.muzero'], | ||
), | ||
) | ||
connect4_muzero_create_config = EasyDict(connect4_muzero_create_config) | ||
create_config = connect4_muzero_create_config | ||
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if __name__ == "__main__": | ||
from lzero.entry import train_muzero | ||
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train_muzero([main_config, create_config], seed=1, max_env_step=max_env_step) |