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from easydict import EasyDict
ant_ddpg_config = dict(
exp_name='ant_ddpg_seed0',
env=dict(
env_id='Ant-v3',
env_wrapper='mujoco_default',
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,
manager=dict(shared_memory=False, ),
# The path to save the game replay
# replay_path='./ant_ddpg_seed0/video',
),
policy=dict(
cuda=True,
load_path="./ant_ddpg_seed0/ckpt/ckpt_best.pth.tar",
random_collect_size=25000,
model=dict(
obs_shape=111,
action_shape=8,
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, # discount_factor: 0.97-0.99
actor_update_freq=1,
noise=False,
),
collect=dict(
n_sample=1,
unroll_len=1,
noise_sigma=0.1,
),
other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
)
)
ant_ddpg_config = EasyDict(ant_ddpg_config)
main_config = ant_ddpg_config
ant_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', ),
)
ant_ddpg_create_config = EasyDict(ant_ddpg_create_config)
create_config = ant_ddpg_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c ant_ddpg_config.py -s 0 --env-step 1e7`
from ding.entry import serial_pipeline
serial_pipeline((main_config, create_config), seed=0)
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