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from easydict import EasyDict
gym_hybrid_ddpg_config = dict(
exp_name='gym_hybrid_ddpg_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=5,
# (bool) Scale output action into legal range [-1, 1].
act_scale=True,
env_id='Moving-v0', # ['Sliding-v0', 'Moving-v0']
n_evaluator_episode=5,
stop_value=1.8,
),
policy=dict(
cuda=True,
priority=False,
random_collect_size=0, # hybrid action space not support random collect now
action_space='hybrid',
model=dict(
obs_shape=10,
action_shape=dict(
action_type_shape=3,
action_args_shape=2,
),
twin_critic=False,
action_space='hybrid',
),
learn=dict(
update_per_collect=10, # 5~10
batch_size=32,
discount_factor=0.99,
learning_rate_actor=0.0003, # 0.001 ~ 0.0003
learning_rate_critic=0.001,
actor_update_freq=1,
noise=False,
),
collect=dict(
n_sample=32,
noise_sigma=0.1,
collector=dict(collect_print_freq=1000, ),
),
eval=dict(evaluator=dict(eval_freq=1000, ), ),
other=dict(
eps=dict(
type='exp',
start=1.,
end=0.1,
decay=100000,
),
replay_buffer=dict(replay_buffer_size=100000, ),
),
),
)
gym_hybrid_ddpg_config = EasyDict(gym_hybrid_ddpg_config)
main_config = gym_hybrid_ddpg_config
gym_hybrid_ddpg_create_config = dict(
env=dict(
type='gym_hybrid',
import_names=['dizoo.gym_hybrid.envs.gym_hybrid_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='ddpg'),
)
gym_hybrid_ddpg_create_config = EasyDict(gym_hybrid_ddpg_create_config)
create_config = gym_hybrid_ddpg_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c gym_hybrid_ddpg_config.py -s 0`
from ding.entry import serial_pipeline
serial_pipeline([main_config, create_config], seed=0, max_env_step=int(1e7))
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