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from easydict import EasyDict
halfcheetah_gcl_sac_config = dict(
exp_name='halfcheetah_gcl_sac_seed0',
env=dict(
env_id='HalfCheetah-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=12000,
),
reward_model=dict(
learning_rate=0.001,
input_size=23,
batch_size=32,
action_shape=6,
continuous=True,
update_per_collect=20,
),
policy=dict(
cuda=False,
on_policy=False,
random_collect_size=0,
model=dict(
obs_shape=17,
action_shape=6,
twin_critic=True,
action_space='reparameterization',
actor_head_hidden_size=256,
critic_head_hidden_size=256,
),
learn=dict(
update_per_collect=1,
batch_size=256,
learning_rate_q=1e-3,
learning_rate_policy=1e-3,
learning_rate_alpha=3e-4,
ignore_done=True,
target_theta=0.005,
discount_factor=0.99,
alpha=0.2,
reparameterization=True,
auto_alpha=False,
),
collect=dict(
# 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``.
model_path='model_path_placeholder',
# If you need the data collected by the collector to contain logit key which reflect the probability of
# the action, you can change the key to be True.
# In Guided cost Learning, we need to use logit to train the reward model, we change the key to be True.
collector_logit=True,
n_sample=256,
unroll_len=1,
),
command=dict(),
eval=dict(),
other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
),
)
halfcheetah_gcl_sac_config = EasyDict(halfcheetah_gcl_sac_config)
main_config = halfcheetah_gcl_sac_config
halfcheetah_gcl_sac_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='sac',
import_names=['ding.policy.sac'],
),
replay_buffer=dict(type='naive', ),
reward_model=dict(type='guided_cost'),
)
halfcheetah_gcl_sac_create_config = EasyDict(halfcheetah_gcl_sac_create_config)
create_config = halfcheetah_gcl_sac_create_config
if __name__ == '__main__':
from ding.entry import serial_pipeline_guided_cost
serial_pipeline_guided_cost((main_config, create_config), seed=0)
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