File size: 2,069 Bytes
079c32c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
from easydict import EasyDict
qbert_a2c_config = dict(
exp_name='qbert_a2c_seed0',
env=dict(
collector_env_num=16,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=1000000,
env_id='QbertNoFrameskip-v4',
#'ALE/Qbert-v5' is available. But special setting is needed after gym make.
frame_stack=4
),
policy=dict(
cuda=True,
model=dict(
obs_shape=[4, 84, 84],
action_shape=6,
encoder_hidden_size_list=[32, 64, 64, 256],
actor_head_hidden_size=256,
critic_head_hidden_size=256,
critic_head_layer_num=2,
),
learn=dict(
batch_size=300,
# (bool) Whether to normalize advantage. Default to False.
adv_norm=False,
learning_rate=0.0001414,
# (float) loss weight of the value network, the weight of policy network is set to 1
value_weight=0.5,
# (float) loss weight of the entropy regularization, the weight of policy network is set to 1
entropy_weight=0.01,
grad_norm=0.5,
betas=(0.0, 0.99),
),
collect=dict(
# (int) collect n_sample data, train model 1 times
n_sample=160,
# (float) the trade-off factor lambda to balance 1step td and mc
gae_lambda=0.99,
discount_factor=0.99,
),
eval=dict(evaluator=dict(eval_freq=500, )),
),
)
main_config = EasyDict(qbert_a2c_config)
qbert_a2c_create_config = dict(
env=dict(
type='atari',
import_names=['dizoo.atari.envs.atari_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='a2c'),
replay_buffer=dict(type='naive'),
)
create_config = EasyDict(qbert_a2c_create_config)
if __name__ == '__main__':
# or you can enter ding -m serial_onpolicy -c qbert_a2c_config.py -s 0
from ding.entry import serial_pipeline_onpolicy
serial_pipeline_onpolicy((main_config, create_config), seed=0)
|