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from easydict import EasyDict
collector_env_num = 8
evaluator_env_num = 5
gfootball_dqn_main_config = dict(
exp_name='gfootball_dqn_seed0',
env=dict(
collector_env_num=collector_env_num,
evaluator_env_num=evaluator_env_num,
n_evaluator_episode=evaluator_env_num,
stop_value=999,
env_name="11_vs_11_easy_stochastic",
# env_name="11_vs_11_stochastic", # default: medium
# env_name="11_vs_11_hard_stochastic",
save_replay_gif=False,
manager=dict(shared_memory=False, ),
),
policy=dict(
cuda=True,
nstep=5,
discount_factor=0.997,
model=dict(),
learn=dict(
update_per_collect=20,
batch_size=512,
learning_rate=0.0001,
target_update_freq=500,
),
collect=dict(n_sample=256),
eval=dict(evaluator=dict(eval_freq=5000)),
other=dict(
eps=dict(
type='exp',
start=1,
end=0.05,
decay=int(2e5),
),
replay_buffer=dict(replay_buffer_size=int(5e5), ),
),
),
)
gfootball_dqn_main_config = EasyDict(gfootball_dqn_main_config)
main_config = gfootball_dqn_main_config
gfootball_dqn_create_config = dict(
env=dict(
type='gfootball',
import_names=['dizoo.gfootball.envs.gfootball_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='dqn'),
)
gfootball_dqn_create_config = EasyDict(gfootball_dqn_create_config)
create_config = gfootball_dqn_create_config
if __name__ == '__main__':
from ding.entry import serial_pipeline
from dizoo.gfootball.model.q_network.football_q_network import FootballNaiveQ
football_naive_q = FootballNaiveQ()
serial_pipeline((main_config, create_config), model=football_naive_q, seed=0, max_env_step=int(5e6))
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