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from easydict import EasyDict
hopper_gcl_config = dict(
exp_name='hopper_gcl_seed0',
env=dict(
env_id='Hopper-v3',
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=4,
evaluator_env_num=10,
n_evaluator_episode=10,
stop_value=3000,
),
reward_model=dict(
learning_rate=0.001,
input_size=14,
batch_size=32,
action_shape=3,
continuous=True,
update_per_collect=20,
),
policy=dict(
cuda=False,
recompute_adv=True,
action_space='continuous',
model=dict(
obs_shape=11,
action_shape=3,
action_space='continuous',
),
learn=dict(
update_per_collect=10,
batch_size=64,
learning_rate=3e-4,
value_weight=0.5,
entropy_weight=0.0,
clip_ratio=0.2,
adv_norm=True,
),
collect=dict(
# Users should add their own model path here. Model path should lead to a model.
# Absolute path is recommended.
# In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``.
model_path='model_path_placeholder',
# If you need the data collected by the collector to contain logit key which reflect the probability of
# the action, you can change the key to be True.
# In Guided cost Learning, we need to use logit to train the reward model, we change the key to be True.
collector_logit=True,
n_sample=2048,
unroll_len=1,
discount_factor=0.99,
gae_lambda=0.97,
),
eval=dict(evaluator=dict(eval_freq=100, )),
),
)
hopper_gcl_config = EasyDict(hopper_gcl_config)
main_config = hopper_gcl_config
hopper_gcl_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='ppo', ),
reward_model=dict(type='guided_cost'),
)
hopper_gcl_create_config = EasyDict(hopper_gcl_create_config)
create_config = hopper_gcl_create_config
if __name__ == '__main__':
from ding.entry import serial_pipeline_guided_cost
serial_pipeline_guided_cost((main_config, create_config), seed=0)
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