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from easydict import EasyDict
cartpole_acer_config = dict(
exp_name='cartpole_acer_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=5,
n_evaluator_episode=5,
stop_value=195,
),
policy=dict(
cuda=False,
model=dict(
obs_shape=4,
action_shape=2,
encoder_hidden_size_list=[64, 64],
),
# (int) the trajectory length to calculate Q retrace target
unroll_len=32,
learn=dict(
# (int) collect n_sample data, train model update_per_collect times
# here we follow ppo serial pipeline
update_per_collect=4,
# (int) the number of data for a train iteration
batch_size=16,
learning_rate_actor=0.0005,
learning_rate_critic=0.0005,
# (float) loss weight of the entropy regularization, the weight of policy network is set to 1
# entropy_weight=0.0001,
entropy_weight=0.0,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.9,
# (float) additional discounting parameter
# (int) the trajectory length to calculate v-trace target
# (float) clip ratio of importance weights
trust_region=True,
c_clip_ratio=10,
# (float) clip ratio of importance sampling
),
collect=dict(
# (int) collect n_sample data, train model n_iteration times
n_sample=16,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.9,
collector=dict(collect_print_freq=1000, ),
),
eval=dict(evaluator=dict(eval_freq=200, )),
other=dict(replay_buffer=dict(replay_buffer_size=10000, ), ),
),
)
cartpole_acer_config = EasyDict(cartpole_acer_config)
main_config = cartpole_acer_config
cartpole_acer_create_config = dict(
env=dict(
type='cartpole',
import_names=['dizoo.classic_control.cartpole.envs.cartpole_env'],
),
env_manager=dict(type='base'),
policy=dict(type='acer'),
)
cartpole_acer_create_config = EasyDict(cartpole_acer_create_config)
create_config = cartpole_acer_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c cartpole_acer_config.py -s 0`
from ding.entry import serial_pipeline
serial_pipeline((main_config, create_config), seed=0)
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