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from easydict import EasyDict
agent_num = 8
collector_env_num = 16
evaluator_env_num = 8
main_config = dict(
exp_name='smac_3s5z_coma_seed0',
env=dict(
map_name='3s5z',
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,
manager=dict(
shared_memory=False,
reset_timeout=6000,
),
),
policy=dict(
model=dict(
# (int) agent_num: The number of the agent.
# For SMAC 3s5z, agent_num=8; for 2c_vs_64zg, agent_num=2.
agent_num=agent_num,
# (int) obs_shape: The shapeension of observation of each agent.
# For 3s5z, obs_shape=150; for 2c_vs_64zg, agent_num=404.
# (int) global_obs_shape: The shapeension of global observation.
# For 3s5z, obs_shape=216; for 2c_vs_64zg, agent_num=342.
obs_shape=dict(
agent_state=150,
global_state=216,
),
# (int) action_shape: The number of action which each agent can take.
# action_shape= the number of common action (6) + the number of enemies.
# For 3s5z, obs_shape=14 (6+8); for 2c_vs_64zg, agent_num=70 (6+64).
action_shape=14,
# (List[int]) The size of hidden layer
actor_hidden_size_list=[64],
),
# used in state_num of hidden_state
collect=dict(
n_episode=32,
env_num=collector_env_num,
),
eval=dict(env_num=evaluator_env_num, evaluator=dict(eval_freq=100, )),
other=dict(
eps=dict(
type='exp',
start=0.5,
end=0.01,
decay=200000,
),
replay_buffer=dict(
# (int) max size of replay buffer
replay_buffer_size=5000,
# (int) max use count of data, if count is bigger than this value, the data will be removed from buffer
max_use=10,
),
),
),
)
main_config = EasyDict(main_config)
create_config = dict(
env=dict(
type='smac',
import_names=['dizoo.smac.envs.smac_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='coma'),
collector=dict(type='episode', get_train_sample=True),
)
create_config = EasyDict(create_config)
if __name__ == '__main__':
from ding.entry import serial_pipeline
serial_pipeline((main_config, create_config), seed=0)
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