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# You can conduct Experiments on D4RL with this config file through the following command:
# cd ../entry && python d4rl_td3_bc_main.py
from easydict import EasyDict
main_config = dict(
exp_name='halfcheetah_expert_td3-bc_seed0',
env=dict(
env_id='halfcheetah-expert-v2',
norm_obs=dict(
use_norm=True,
offline_stats=dict(use_offline_stats=True, ),
),
collector_env_num=1,
evaluator_env_num=8,
use_act_scale=True,
n_evaluator_episode=8,
stop_value=6000,
),
policy=dict(
cuda=True,
model=dict(
obs_shape=17,
action_shape=6,
),
learn=dict(
train_epoch=30000,
batch_size=256,
learning_rate_actor=0.0003,
learning_rate_critic=0.0003,
actor_update_freq=2,
noise=True,
noise_sigma=0.2,
noise_range={
'min': -0.5,
'max': 0.5
},
alpha=2.5,
),
collect=dict(
data_type='d4rl',
data_path=None,
),
eval=dict(evaluator=dict(eval_freq=10000, )),
other=dict(replay_buffer=dict(replay_buffer_size=2000000, ), ),
),
)
main_config = EasyDict(main_config)
main_config = main_config
create_config = dict(
env=dict(
type='d4rl',
import_names=['dizoo.d4rl.envs.d4rl_env'],
),
env_manager=dict(
cfg_type='BaseEnvManagerDict',
type='base',
),
policy=dict(
type='td3_bc',
import_names=['ding.policy.td3_bc'],
),
replay_buffer=dict(type='naive', ),
)
create_config = EasyDict(create_config)
create_config = create_config
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