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from ding.entry import serial_pipeline
from easydict import EasyDict
agent_num = 10
collector_env_num = 4
evaluator_env_num = 8
main_config = dict(
exp_name='smac_MMM_madqn_seed0',
env=dict(
map_name='MMM',
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,
stop_value=0.999,
n_evaluator_episode=32,
special_global_state=True,
manager=dict(shared_memory=False, ),
),
policy=dict(
nstep=1,
model=dict(
agent_num=agent_num,
obs_shape=186,
global_obs_shape=389,
global_cooperation=True,
action_shape=16,
hidden_size_list=[256, 256],
),
learn=dict(
update_per_collect=20,
batch_size=64,
learning_rate=0.0005,
clip_value=5,
target_update_theta=0.008,
discount_factor=0.95,
),
collect=dict(
collector=dict(get_train_sample=True, ),
n_episode=32,
unroll_len=10,
env_num=collector_env_num,
),
eval=dict(env_num=evaluator_env_num, evaluator=dict(eval_freq=1000, )),
other=dict(
eps=dict(
type='linear',
start=1,
end=0.05,
decay=10000,
),
replay_buffer=dict(replay_buffer_size=15000, ),
),
),
)
main_config = EasyDict(main_config)
create_config = dict(
env=dict(
type='smac',
import_names=['dizoo.smac.envs.smac_env'],
),
env_manager=dict(type='base'),
policy=dict(type='madqn'),
collector=dict(type='episode'),
)
create_config = EasyDict(create_config)
def train(args):
config = [main_config, create_config]
serial_pipeline(config, seed=args.seed, max_env_step=1e7)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--seed', '-s', type=int, default=0)
args = parser.parse_args()
train(args)
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