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from easydict import EasyDict
agent_num = 3
collector_env_num = 8
evaluator_env_num = 8
special_global_state = True
SMAC_3m_masac_default_config = dict(
exp_name='smac_3m_masac_seed0',
env=dict(
map_name='3m',
difficulty=7,
reward_only_positive=True,
mirror_opponent=False,
agent_num=agent_num,
collector_env_num=collector_env_num,
evaluator_env_num=evaluator_env_num,
n_evaluator_episode=16,
stop_value=0.99,
death_mask=True,
special_global_state=special_global_state,
manager=dict(
shared_memory=False,
reset_timeout=6000,
),
),
policy=dict(
cuda=True,
random_collect_size=0,
model=dict(
agent_obs_shape=42,
global_obs_shape=77,
action_shape=9,
twin_critic=True,
actor_head_hidden_size=256,
critic_head_hidden_size=256,
),
learn=dict(
update_per_collect=50,
batch_size=320,
learning_rate_q=5e-4,
learning_rate_policy=5e-4,
learning_rate_alpha=5e-4,
ignore_done=False,
target_theta=0.005,
discount_factor=0.99,
alpha=0.2,
auto_alpha=True,
log_space=True,
),
collect=dict(
env_num=collector_env_num,
n_sample=3200,
unroll_len=1,
),
eval=dict(
evaluator=dict(eval_freq=50, ),
env_num=evaluator_env_num,
),
other=dict(
eps=dict(
type='linear',
start=1,
end=0.05,
decay=100000,
), # TODO(pu)
replay_buffer=dict(replay_buffer_size=1000000, ),
),
),
)
SMAC_3m_masac_default_config = EasyDict(SMAC_3m_masac_default_config)
main_config = SMAC_3m_masac_default_config
SMAC_3m_masac_default_create_config = dict(
env=dict(
type='smac',
import_names=['dizoo.smac.envs.smac_env'],
),
env_manager=dict(type='base'),
policy=dict(type='sac_discrete', ),
)
SMAC_3m_masac_default_create_config = EasyDict(SMAC_3m_masac_default_create_config)
create_config = SMAC_3m_masac_default_create_config
if __name__ == '__main__':
from ding.entry import serial_pipeline
serial_pipeline((main_config, create_config), seed=0)
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