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from easydict import EasyDict
collector_env_num = 1
evaluator_env_num = 1
walker2d_onppo_config = dict(
exp_name='walker2d_onppo_seed0',
env=dict(
env_id='Walker2d-v3',
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=collector_env_num,
evaluator_env_num=evaluator_env_num,
n_evaluator_episode=10,
stop_value=6000,
),
policy=dict(
cuda=True,
recompute_adv=True,
action_space='continuous',
model=dict(
action_space='continuous',
obs_shape=17,
action_shape=6,
),
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=True,
ignore_done=False,
grad_clip_type='clip_norm',
grad_clip_value=0.5,
),
collect=dict(
collector_env_num=collector_env_num,
n_sample=3200,
unroll_len=1,
discount_factor=0.99,
gae_lambda=0.95,
),
eval=dict(evaluator=dict(eval_freq=500, )),
),
)
walker2d_onppo_config = EasyDict(walker2d_onppo_config)
main_config = walker2d_onppo_config
walker2d_onppo_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='base'),
# env_manager=dict(type='subprocess'),
policy=dict(type='ppo', ),
)
walker2d_onppo_create_config = EasyDict(walker2d_onppo_create_config)
create_config = walker2d_onppo_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial_onpolicy -c walker2d_onppo_config.py -s 0`
from ding.entry import serial_pipeline_onpolicy
serial_pipeline_onpolicy([main_config, create_config], seed=0)
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