File size: 2,154 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 64 65 66 67 68 |
from easydict import EasyDict
ant_ppo_config = dict(
exp_name="ant_onppo_seed0",
env=dict(
env_id='Ant-v3',
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=10,
evaluator_env_num=10,
n_evaluator_episode=10,
stop_value=6000,
manager=dict(shared_memory=False, )
),
policy=dict(
cuda=True,
recompute_adv=True,
action_space='continuous',
model=dict(
action_space='continuous',
obs_shape=111,
action_shape=8,
),
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,
# When we recompute advantage, we need the key done in data to split trajectories, 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'. 'traj_flag' indicates termination of trajectory.
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, )),
),
)
ant_ppo_config = EasyDict(ant_ppo_config)
main_config = ant_ppo_config
ant_ppo_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='ppo'),
)
ant_ppo_create_config = EasyDict(ant_ppo_create_config)
create_config = ant_ppo_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial_onpolicy -c ant_onppo_config.py -s 0 --env-step 1e7`
from ding.entry import serial_pipeline_onpolicy
serial_pipeline_onpolicy((main_config, create_config), seed=0)
|