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from easydict import EasyDict
obs_shape = 24
act_shape = 4
bipedalwalker_sac_gail_default_config = dict(
exp_name='bipedalwalker_sac_gail_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=5,
# (bool) Scale output action into legal range.
act_scale=True,
n_evaluator_episode=5,
stop_value=300,
rew_clip=True,
# The path to save the game replay
replay_path=None,
),
reward_model=dict(
type='gail',
input_size=obs_shape + act_shape,
hidden_size=64,
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(
cuda=False,
priority=False,
random_collect_size=1000,
model=dict(
obs_shape=obs_shape,
action_shape=act_shape,
twin_critic=True,
action_space='reparameterization',
actor_head_hidden_size=128,
critic_head_hidden_size=128,
),
learn=dict(
update_per_collect=1,
batch_size=128,
learning_rate_q=0.001,
learning_rate_policy=0.001,
learning_rate_alpha=0.0003,
ignore_done=True,
target_theta=0.005,
discount_factor=0.99,
auto_alpha=True,
value_network=False,
),
collect=dict(
n_sample=128,
unroll_len=1,
),
other=dict(replay_buffer=dict(replay_buffer_size=100000, ), ),
),
)
bipedalwalker_sac_gail_default_config = EasyDict(bipedalwalker_sac_gail_default_config)
main_config = bipedalwalker_sac_gail_default_config
bipedalwalker_sac_gail_create_config = dict(
env=dict(
type='bipedalwalker',
import_names=['dizoo.box2d.bipedalwalker.envs.bipedalwalker_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='sac',
import_names=['ding.policy.sac'],
),
replay_buffer=dict(type='naive', ),
)
bipedalwalker_sac_gail_create_config = EasyDict(bipedalwalker_sac_gail_create_config)
create_config = bipedalwalker_sac_gail_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial_gail -c bipedalwalker_sac_gail_config.py -s 0`
# then input the config you used to generate your expert model in the path mentioned above
# e.g. bipedalwalker_sac_config.py
from ding.entry import serial_pipeline_gail
from dizoo.box2d.bipedalwalker.config import bipedalwalker_sac_config, bipedalwalker_sac_create_config
expert_main_config = bipedalwalker_sac_config
expert_create_config = bipedalwalker_sac_create_config
serial_pipeline_gail(
[main_config, create_config], [expert_main_config, expert_create_config], seed=0, collect_data=True
)
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