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from easydict import EasyDict
halfcheetah_d4pg_config = dict(
exp_name='halfcheetah_d4pg_seed0',
env=dict(
env_id='HalfCheetah-v3',
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=4,
evaluator_env_num=4,
n_evaluator_episode=8,
stop_value=20000,
),
policy=dict(
cuda=True,
priority=True,
nstep=5,
random_collect_size=10000,
model=dict(
obs_shape=17,
action_shape=6,
actor_head_hidden_size=512,
critic_head_hidden_size=512,
action_space='regression',
critic_head_type='categorical',
v_min=0,
v_max=5000, # v_max: [3000, 10000]
n_atom=51,
),
learn=dict(
update_per_collect=4, # update_per_collect: [1, 4]
batch_size=256,
learning_rate_actor=3e-4,
learning_rate_critic=3e-4,
ignore_done=True,
target_theta=0.005,
discount_factor=0.99,
actor_update_freq=1,
noise=False,
),
collect=dict(
n_sample=8,
unroll_len=1,
noise_sigma=0.2, # noise_sigma: [0.1, 0.2]
),
other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
)
)
halfcheetah_d4pg_config = EasyDict(halfcheetah_d4pg_config)
main_config = halfcheetah_d4pg_config
halfcheetah_d4pg_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='d4pg',
import_names=['ding.policy.d4pg'],
),
)
halfcheetah_d4pg_create_config = EasyDict(halfcheetah_d4pg_create_config)
create_config = halfcheetah_d4pg_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c halfcheetah_d4pg_config.py -s 0`
from ding.entry import serial_pipeline
serial_pipeline((main_config, create_config), seed=0)
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