File size: 2,544 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 69 70 71 72 73 74 75 76 77 78 79 80 81 |
from easydict import EasyDict
ant_trex_ppo_config = dict(
exp_name='ant_trex_onppo_seed0',
env=dict(
manager=dict(shared_memory=True, reset_inplace=True),
env_id='Ant-v3',
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=8,
evaluator_env_num=10,
n_evaluator_episode=10,
stop_value=6000,
),
reward_model=dict(
type='trex',
min_snippet_length=10,
max_snippet_length=100,
checkpoint_min=100,
checkpoint_max=900,
checkpoint_step=100,
learning_rate=1e-5,
update_per_collect=1,
# Users should add their own model path here. Model path should lead to a model.
# Absolute path is recommended.
# In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``.
expert_model_path='model_path_placeholder',
# Path where to store the reward model
reward_model_path='abs_data_path + ./ant.params',
continuous=True,
# Path to the offline dataset
# See ding/entry/application_entry_trex_collect_data.py to collect the data
offline_data_path='abs_data_path',
),
policy=dict(
cuda=True,
recompute_adv=True,
model=dict(
obs_shape=111,
action_shape=8,
action_space='continuous',
),
action_space='continuous',
learn=dict(
epoch_per_collect=10,
batch_size=64,
learning_rate=3e-4,
value_weight=0.5,
entropy_weight=0.0,
clip_ratio=0.2,
adv_norm=True,
value_norm=True,
),
collect=dict(
n_sample=2048,
unroll_len=1,
discount_factor=0.99,
gae_lambda=0.97,
),
eval=dict(evaluator=dict(eval_freq=5000, )),
),
)
ant_trex_ppo_config = EasyDict(ant_trex_ppo_config)
main_config = ant_trex_ppo_config
ant_trex_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_trex_ppo_create_config = EasyDict(ant_trex_ppo_create_config)
create_config = ant_trex_ppo_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c ant_trex_onppo_config.py -s 0`
from ding.entry import serial_pipeline_trex_onpolicy
serial_pipeline_trex_onpolicy((main_config, create_config), seed=0)
|