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from easydict import EasyDict
cartpole_gcl_ppo_onpolicy_config = dict(
exp_name='cartpole_gcl_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=5,
n_evaluator_episode=5,
stop_value=195,
),
reward_model=dict(
learning_rate=0.001,
input_size=5,
batch_size=32,
continuous=False,
update_per_collect=10,
),
policy=dict(
cuda=False,
recompute_adv=True,
action_space='discrete',
model=dict(
obs_shape=4,
action_shape=2,
action_space='discrete',
encoder_hidden_size_list=[64, 64, 128],
critic_head_hidden_size=128,
actor_head_hidden_size=128,
),
learn=dict(
update_per_collect=2,
batch_size=64,
learning_rate=0.001,
entropy_weight=0.01,
),
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, # add logit into collected transition
n_sample=256,
discount_factor=0.9,
gae_lambda=0.95,
),
eval=dict(evaluator=dict(eval_freq=50, ), ),
),
)
cartpole_gcl_ppo_onpolicy_config = EasyDict(cartpole_gcl_ppo_onpolicy_config)
main_config = cartpole_gcl_ppo_onpolicy_config
cartpole_gcl_ppo_onpolicy_create_config = dict(
env=dict(
type='cartpole',
import_names=['dizoo.classic_control.cartpole.envs.cartpole_env'],
),
env_manager=dict(type='base'),
policy=dict(type='ppo'),
reward_model=dict(type='guided_cost'),
)
cartpole_gcl_ppo_onpolicy_create_config = EasyDict(cartpole_gcl_ppo_onpolicy_create_config)
create_config = cartpole_gcl_ppo_onpolicy_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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