|
from easydict import EasyDict |
|
from copy import deepcopy |
|
|
|
halfcheetah_dt_config = dict( |
|
exp_name='dt_log/d4rl/halfcheetah/halfcheetah_expert_dt_seed0', |
|
env=dict( |
|
env_id='HalfCheetah-v3', |
|
collector_env_num=1, |
|
evaluator_env_num=8, |
|
use_act_scale=True, |
|
n_evaluator_episode=8, |
|
stop_value=6000, |
|
), |
|
dataset=dict( |
|
env_type='mujoco', |
|
rtg_scale=1000, |
|
context_len=30, |
|
data_dir_prefix='d4rl/halfcheetah_expert-v2.pkl', |
|
), |
|
policy=dict( |
|
cuda=True, |
|
stop_value=6000, |
|
state_mean=None, |
|
state_std=None, |
|
evaluator_env_num=8, |
|
env_name='HalfCheetah-v3', |
|
rtg_target=6000, |
|
max_eval_ep_len=1000, |
|
wt_decay=1e-4, |
|
warmup_steps=10000, |
|
context_len=20, |
|
weight_decay=0.1, |
|
clip_grad_norm_p=0.25, |
|
model=dict( |
|
state_dim=11, |
|
act_dim=3, |
|
n_blocks=3, |
|
h_dim=128, |
|
context_len=20, |
|
n_heads=1, |
|
drop_p=0.1, |
|
continuous=True, |
|
), |
|
batch_size=64, |
|
learning_rate=1e-4, |
|
collect=dict( |
|
data_type='d4rl_trajectory', |
|
unroll_len=1, |
|
), |
|
eval=dict(evaluator=dict(eval_freq=100, ), ), |
|
), |
|
) |
|
|
|
halfcheetah_dt_config = EasyDict(halfcheetah_dt_config) |
|
main_config = halfcheetah_dt_config |
|
halfcheetah_dt_create_config = dict( |
|
env=dict( |
|
type='mujoco', |
|
import_names=['dizoo.mujoco.envs.mujoco_env'], |
|
), |
|
env_manager=dict(type='subprocess'), |
|
policy=dict(type='dt'), |
|
) |
|
halfcheetah_dt_create_config = EasyDict(halfcheetah_dt_create_config) |
|
create_config = halfcheetah_dt_create_config |
|
|
|
if __name__ == "__main__": |
|
from ding.entry import serial_pipeline_dt |
|
config = deepcopy([main_config, create_config]) |
|
serial_pipeline_dt(config, seed=0, max_train_iter=1000) |
|
|
