Spaces:
Sleeping
Sleeping
from easydict import EasyDict | |
hopper_bdq_config = dict( | |
exp_name='hopper_bdq_seed0', | |
env=dict( | |
env_id='Hopper-v3', | |
norm_reward=dict(use_norm=False, ), | |
collector_env_num=8, | |
evaluator_env_num=8, | |
n_evaluator_episode=8, | |
stop_value=int(1e6), | |
action_bins_per_branch=4, | |
), | |
policy=dict( | |
cuda=False, | |
priority=False, | |
discount_factor=0.99, | |
nstep=3, | |
model=dict( | |
obs_shape=11, | |
num_branches=3, | |
action_bins_per_branch=4, # mean the action shape is 3, 4 discrete actions for each action dimension | |
encoder_hidden_size_list=[256, 256, 128], | |
), | |
learn=dict( | |
ignore_done=False, | |
batch_size=512, | |
learning_rate=3e-4, | |
# Frequency of target network update. | |
target_update_freq=500, | |
update_per_collect=20, | |
), | |
collect=dict( | |
# You can use either "n_sample" or "n_episode" in collector.collect. | |
# Get "n_sample" samples per collect. | |
n_sample=256, | |
# Cut trajectories into pieces with length "unroll_len". | |
unroll_len=1, | |
), | |
eval=dict(evaluator=dict(eval_freq=1000, )), | |
other=dict( | |
# Epsilon greedy with decay. | |
eps=dict( | |
# Decay type. Support ['exp', 'linear']. | |
type='exp', | |
start=1, | |
end=0.05, | |
decay=int(1e5), | |
), | |
replay_buffer=dict(replay_buffer_size=int(1e6), ) | |
), | |
), | |
) | |
hopper_bdq_config = EasyDict(hopper_bdq_config) | |
main_config = hopper_bdq_config | |
hopper_bdq_create_config = dict( | |
env=dict( | |
type='mujoco', | |
import_names=['dizoo.mujoco.envs.mujoco_env'], | |
), | |
env_manager=dict(type='subprocess'), | |
policy=dict(type='bdq', ), | |
) | |
hopper_bdq_create_config = EasyDict(hopper_bdq_create_config) | |
create_config = hopper_bdq_create_config | |
if __name__ == "__main__": | |
# or you can enter `ding -m serial_onpolicy -c hopper_bdq_config.py -s 0` | |
from ding.entry import serial_pipeline | |
serial_pipeline( | |
[main_config, create_config], | |
seed=0, | |
max_env_step=10000000, | |
) | |