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init space
079c32c
from easydict import EasyDict
qbert_onppo_config = dict(
exp_name='enduro_onppo_seed0',
env=dict(
collector_env_num=16,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=int(1e10),
env_id='QbertNoFrameskip-v4',
#'ALE/Qbert-v5' is available. But special setting is needed after gym make.
frame_stack=4
),
policy=dict(
cuda=True,
recompute_adv=True,
action_space='discrete',
model=dict(
obs_shape=[4, 84, 84],
action_shape=6,
action_space='discrete',
encoder_hidden_size_list=[64, 64, 128],
actor_head_hidden_size=128,
critic_head_hidden_size=128,
),
learn=dict(
epoch_per_collect=10,
update_per_collect=1,
batch_size=320,
learning_rate=3e-4,
value_weight=0.5,
entropy_weight=0.001,
clip_ratio=0.2,
adv_norm=True,
value_norm=True,
# for onppo, when we recompute adv, we need the key done in data to split traj, so we must
# use ignore_done=False here,
# but when we add key traj_flag in data as the backup for key done, we could choose to use ignore_done=True
# for halfcheetah, the length=1000
ignore_done=False,
grad_clip_type='clip_norm',
grad_clip_value=0.5,
),
collect=dict(
n_sample=3200,
unroll_len=1,
discount_factor=0.99,
gae_lambda=0.95,
),
eval=dict(evaluator=dict(eval_freq=5000, )),
),
)
main_config = EasyDict(qbert_onppo_config)
qbert_onppo_create_config = dict(
env=dict(
type='atari',
import_names=['dizoo.atari.envs.atari_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='ppo'),
)
create_config = EasyDict(qbert_onppo_create_config)
if __name__ == "__main__":
# or you can enter ding -m serial_onpolicy -c qbert_onppo_config.py -s 0
from ding.entry import serial_pipeline_onpolicy
serial_pipeline_onpolicy([main_config, create_config], seed=0)