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from easydict import EasyDict
bipedalwalker_td3_config = dict(
exp_name='bipedalwalker_td3_seed0',
env=dict(
env_id='BipedalWalker-v3',
collector_env_num=8,
evaluator_env_num=5,
# (bool) Scale output action into legal range.
act_scale=True,
n_evaluator_episode=5,
rew_clip=True,
),
policy=dict(
cuda=True,
random_collect_size=10000,
model=dict(
obs_shape=24,
action_shape=4,
twin_critic=True,
action_space='regression',
actor_head_hidden_size=400,
critic_head_hidden_size=400,
),
learn=dict(
update_per_collect=64,
batch_size=256,
learning_rate_actor=0.0003,
learning_rate_critic=0.0003,
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(hook=dict(log_show_after_iter=1000, ))
),
collect=dict(n_sample=64, ),
other=dict(replay_buffer=dict(replay_buffer_size=300000, ), ),
),
)
bipedalwalker_td3_config = EasyDict(bipedalwalker_td3_config)
main_config = bipedalwalker_td3_config
bipedalwalker_td3_create_config = dict(
env=dict(
type='bipedalwalker',
import_names=['dizoo.box2d.bipedalwalker.envs.bipedalwalker_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='td3'),
)
bipedalwalker_td3_create_config = EasyDict(bipedalwalker_td3_create_config)
create_config = bipedalwalker_td3_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c bipedalwalker_td3_config.py -s 0`
from ding.entry import serial_pipeline
serial_pipeline([main_config, create_config], seed=0, max_env_step=int(1e5))
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