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from easydict import EasyDict
from copy import deepcopy
hopper_dt_config = dict(
exp_name='dt_log/d4rl/hopper/hopper_medium_expert_dt',
env=dict(
env_id='Hopper-v3',
collector_env_num=1,
evaluator_env_num=8,
use_act_scale=True,
n_evaluator_episode=8,
stop_value=3600,
),
dataset=dict(
env_type='mujoco',
rtg_scale=1000,
context_len=20,
data_dir_prefix='d4rl/hopper_medium_expert.pkl',
),
policy=dict(
cuda=True,
stop_value=3600,
state_mean=None,
state_std=None,
evaluator_env_num=8,
env_name='Hopper-v3',
rtg_target=3600, # max target return to go
max_eval_ep_len=1000, # max lenght of one episode
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, ), ),
),
)
hopper_dt_config = EasyDict(hopper_dt_config)
main_config = hopper_dt_config
hopper_dt_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='dt'),
)
hopper_dt_create_config = EasyDict(hopper_dt_create_config)
create_config = hopper_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)
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