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from easydict import EasyDict
cartpole_balance_ppo_config = dict(
exp_name='dmc2gym_cartpole_balance_ppo',
env=dict(
env_id='dmc2gym_cartpole_balance',
domain_name='cartpole',
task_name='balance',
from_pixels=False,
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=1,
evaluator_env_num=8,
use_act_scale=True,
n_evaluator_episode=8,
stop_value=1000,
),
policy=dict(
cuda=True,
recompute_adv=True,
action_space='discrete',
model=dict(
obs_shape=5,
action_shape=1,
action_space='discrete',
encoder_hidden_size_list=[64, 64, 128],
critic_head_hidden_size=128,
actor_head_hidden_size=128,
),
learn=dict(
epoch_per_collect=2,
batch_size=64,
learning_rate=0.001,
value_weight=0.5,
entropy_weight=0.01,
clip_ratio=0.2,
learner=dict(hook=dict(save_ckpt_after_iter=100)),
),
collect=dict(
n_sample=256,
unroll_len=1,
discount_factor=0.9,
gae_lambda=0.95,
),
other=dict(replay_buffer=dict(replay_buffer_size=10000, ), ),
)
)
cartpole_balance_ppo_config = EasyDict(cartpole_balance_ppo_config)
main_config = cartpole_balance_ppo_config
cartpole_balance_create_config = dict(
env=dict(
type='dmc2gym',
import_names=['dizoo.dmc2gym.envs.dmc2gym_env'],
),
env_manager=dict(type='base'),
policy=dict(type='ppo'),
replay_buffer=dict(type='naive', ),
)
cartpole_balance_create_config = EasyDict(cartpole_balance_create_config)
create_config = cartpole_balance_create_config
# To use this config, you can enter dizoo/dmc2gym/entry to call dmc2gym_onppo_main.py
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