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from easydict import EasyDict
qbert_impala_config = dict(
exp_name='qbert_impala_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=10000000000,
env_id='QbertNoFrameskip-v4',
#'ALE/Qbert-v5' is available. But special setting is needed after gym make.
frame_stack=4
),
policy=dict(
cuda=True,
# (int) the trajectory length to calculate v-trace target
unroll_len=32,
model=dict(
obs_shape=[4, 84, 84],
action_shape=6,
encoder_hidden_size_list=[128, 128, 256, 512],
critic_head_hidden_size=512,
critic_head_layer_num=3,
actor_head_hidden_size=512,
actor_head_layer_num=3,
),
learn=dict(
# (int) collect n_sample data, train model update_per_collect times
# here we follow impala serial pipeline
update_per_collect=10, # update_per_collect show be in [1, 10]
# (int) the number of data for a train iteration
batch_size=128,
grad_clip_type='clip_norm',
clip_value=5,
learning_rate=0.0003,
# (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,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.99,
# (float) additional discounting parameter
lambda_=0.95,
# (float) clip ratio of importance weights
rho_clip_ratio=1.0,
# (float) clip ratio of importance weights
c_clip_ratio=1.0,
# (float) clip ratio of importance sampling
rho_pg_clip_ratio=1.0,
),
collect=dict(
# (int) collect n_sample data, train model n_iteration times
n_sample=16,
collector=dict(collect_print_freq=1000, ),
),
eval=dict(evaluator=dict(eval_freq=5000, )),
other=dict(replay_buffer=dict(replay_buffer_size=10000, ), ),
),
)
main_config = EasyDict(qbert_impala_config)
qbert_impala_create_config = dict(
env=dict(
type='atari',
import_names=['dizoo.atari.envs.atari_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='impala'),
replay_buffer=dict(type='naive'),
)
create_config = EasyDict(qbert_impala_create_config)
if __name__ == '__main__':
# or you can enter ding -m serial -c qbert_impala_config.py -s 0
from ding.entry import serial_pipeline
serial_pipeline((main_config, create_config), seed=0)
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