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from easydict import EasyDict | |
bridgewalker_ddpg_config = dict( | |
exp_name='evogym_bridgewalker_ddpg_seed0', | |
env=dict( | |
env_id='BridgeWalker-v0', | |
robot='speed_bot', | |
robot_dir='../envs', | |
collector_env_num=8, | |
evaluator_env_num=8, | |
n_evaluator_episode=8, | |
stop_value=10, | |
manager=dict(shared_memory=True, ), | |
# The path to save the game replay | |
# replay_path='./evogym_walker_ddpg_seed0/video', | |
), | |
policy=dict( | |
cuda=True, | |
# load_path="./evogym_walker_ddpg_seed0/ckpt/ckpt_best.pth.tar", | |
random_collect_size=1000, | |
model=dict( | |
obs_shape=59, | |
action_shape=10, | |
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, ), ), | |
) | |
) | |
bridgewalker_ddpg_config = EasyDict(bridgewalker_ddpg_config) | |
main_config = bridgewalker_ddpg_config | |
bridgewalker_ddpg_create_config = dict( | |
env=dict( | |
type='evogym', | |
import_names=['dizoo.evogym.envs.evogym_env'], | |
), | |
env_manager=dict(type='subprocess'), | |
policy=dict( | |
type='ddpg', | |
import_names=['ding.policy.ddpg'], | |
), | |
replay_buffer=dict(type='naive', ), | |
) | |
bridgewalker_ddpg_create_config = EasyDict(bridgewalker_ddpg_create_config) | |
create_config = bridgewalker_ddpg_create_config | |
if __name__ == "__main__": | |
# or you can enter `ding -m serial -c evogym_bridgewalker_ddpg_config.py -s 0 --env-step 1e7` | |
from ding.entry import serial_pipeline | |
serial_pipeline((main_config, create_config), seed=0) | |