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from easydict import EasyDict
hopper_sac_data_generation_config = dict(
exp_name='hopper_sac_data_generation_seed0',
env=dict(
env_id='Hopper-v3',
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=10,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=6000,
),
policy=dict(
cuda=True,
random_collect_size=10000,
model=dict(
obs_shape=11,
action_shape=3,
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=False,
target_theta=0.005,
discount_factor=0.99,
alpha=0.2,
reparameterization=True,
auto_alpha=False,
learner=dict(
# 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='model_path_placeholder',
hook=dict(
load_ckpt_before_run='model_path_placeholder',
save_ckpt_after_run=False,
)
),
),
collect=dict(
n_sample=1,
unroll_len=1,
# 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
save_path='data_path_placeholder',
),
command=dict(),
eval=dict(),
other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
),
)
hopper_sac_data_generation_config = EasyDict(hopper_sac_data_generation_config)
main_config = hopper_sac_data_generation_config
hopper_sac_data_genearation_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', ),
)
hopper_sac_data_genearation_create_config = EasyDict(hopper_sac_data_genearation_create_config)
create_config = hopper_sac_data_genearation_create_config
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