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from easydict import EasyDict
obs_shape = 17
act_shape = 6
walker2d_sqil_config = dict(
exp_name='walker2d_sqil_sac_seed0',
env=dict(
env_id='Walker2d-v3',
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=1,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=6000,
),
policy=dict(
cuda=True,
random_collect_size=25000,
expert_random_collect_size=10000,
model=dict(
obs_shape=obs_shape,
action_shape=act_shape,
twin_critic=True,
action_space='reparameterization',
actor_head_hidden_size=256,
critic_head_hidden_size=256,
),
nstep=1,
discount_factor=0.97,
learn=dict(
update_per_collect=1,
batch_size=64,
learning_rate_q=1e-3,
learning_rate_policy=1e-3,
learning_rate_alpha=3e-4,
ignore_done=False,
target_theta=0.005,
discount_factor=0.99,
alpha=0.2,
reparameterization=True,
auto_alpha=True,
),
collect=dict(
n_sample=16,
unroll_len=1,
model_path='model_path_placeholder',
),
eval=dict(evaluator=dict(eval_freq=500, )),
other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
),
)
walker2d_sqil_config = EasyDict(walker2d_sqil_config)
main_config = walker2d_sqil_config
walker2d_sqil_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='sqil_sac', ),
replay_buffer=dict(type='naive', ),
)
walker2d_sqil_create_config = EasyDict(walker2d_sqil_create_config)
create_config = walker2d_sqil_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial_sqil -c walker2d_sqil_sac_config.py -s 0`
# then input the config you used to generate your expert model in the path mentioned above
# e.g. walker2d_sac_config.py
from ding.entry import serial_pipeline_sqil
from dizoo.mujoco.config.walker2d_sac_config import walker2d_sac_config, walker2d_sac_create_config
expert_main_config = walker2d_sac_config
expert_create_config = walker2d_sac_create_config
serial_pipeline_sqil(
[main_config, create_config],
[expert_main_config, expert_create_config],
max_env_step=5000000,
seed=0,
)
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