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from easydict import EasyDict
hopper_td3_data_generation_config = dict(
exp_name='hopper_td3_data_generation_seed0',
env=dict(
env_id='Hopper-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=11000,
),
policy=dict(
cuda=True,
random_collect_size=25000,
model=dict(
obs_shape=11,
action_shape=3,
twin_critic=True,
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=True,
target_theta=0.005,
discount_factor=0.99,
actor_update_freq=2,
noise=True,
noise_sigma=0.2,
noise_range=dict(
min=-0.5,
max=0.5,
),
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,
noise_sigma=0.1,
# 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',
data_type='hdf5',
),
other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
)
)
hopper_td3_data_generation_config = EasyDict(hopper_td3_data_generation_config)
main_config = hopper_td3_data_generation_config
hopper_td3_data_generation_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='td3',
import_names=['ding.policy.td3'],
),
replay_buffer=dict(type='naive', ),
)
hopper_td3_data_generation_create_config = EasyDict(hopper_td3_data_generation_create_config)
create_config = hopper_td3_data_generation_create_config
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