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from easydict import EasyDict
walker2d_gail_ddpg_config = dict(
exp_name='walker2d_gail_ddpg_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,
),
reward_model=dict(
input_size=23,
hidden_size=256,
batch_size=64,
learning_rate=1e-3,
update_per_collect=100,
# 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='data_path_placeholder+/reward_model/ckpt/ckpt_best.pth.tar',
# Users should add their own data path here. Data path should lead to a file to store data or load the stored data.
# Absolute path is recommended.
# In DI-engine, it is usually located in ``exp_name`` directory
data_path='data_path_placeholder',
collect_count=100000,
),
policy=dict(
# state_dict of the policy.
# 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``.
load_path='walker2d_ddpg_gail/ckpt/ckpt_best.pth.tar',
cuda=True,
on_policy=False,
random_collect_size=25000,
model=dict(
obs_shape=17,
action_shape=6,
twin_critic=False,
actor_head_hidden_size=256,
critic_head_hidden_size=256,
action_space='regression',
),
learn=dict(
update_per_collect=1,
batch_size=256,
learning_rate_actor=1e-3,
learning_rate_critic=1e-3,
ignore_done=False,
target_theta=0.005,
discount_factor=0.99,
actor_update_freq=1,
noise=False,
),
collect=dict(
n_sample=64,
unroll_len=1,
noise_sigma=0.1,
),
other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
)
)
walker2d_gail_ddpg_config = EasyDict(walker2d_gail_ddpg_config)
main_config = walker2d_gail_ddpg_config
walker2d_gail_ddpg_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='ddpg',
import_names=['ding.policy.ddpg'],
),
replay_buffer=dict(type='naive', ),
)
walker2d_gail_ddpg_create_config = EasyDict(walker2d_gail_ddpg_create_config)
create_config = walker2d_gail_ddpg_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial_gail -c walker2d_gail_ddpg_config.py -s 0`
# then input the config you used to generate your expert model in the path mentioned above
# e.g. walker2d_ddpg_config.py
from ding.entry import serial_pipeline_gail
from dizoo.mujoco.config.walker2d_ddpg_config import walker2d_ddpg_config, walker2d_ddpg_create_config
expert_main_config = walker2d_ddpg_config
expert_create_config = walker2d_ddpg_create_config
serial_pipeline_gail(
[main_config, create_config], [expert_main_config, expert_create_config],
max_env_step=1000000,
seed=0,
collect_data=True
)
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