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from easydict import EasyDict
lunarlander_ppo_config = dict(
exp_name='lunarlander_gcl_seed0',
env=dict(
collector_env_num=8,
evaluator_env_num=8,
env_id='LunarLander-v2',
n_evaluator_episode=8,
stop_value=200,
),
reward_model=dict(
learning_rate=0.001,
input_size=9,
batch_size=32,
continuous=False,
update_per_collect=20,
),
policy=dict(
cuda=False,
action_space='discrete',
recompute_adv=True,
model=dict(
obs_shape=8,
action_shape=4,
action_space='discrete',
),
learn=dict(
update_per_collect=8,
batch_size=800,
learning_rate=0.001,
value_weight=0.5,
entropy_weight=0.01,
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=800,
unroll_len=1,
discount_factor=0.99,
gae_lambda=0.95,
),
),
)
lunarlander_ppo_config = EasyDict(lunarlander_ppo_config)
main_config = lunarlander_ppo_config
lunarlander_ppo_create_config = dict(
env=dict(
type='lunarlander',
import_names=['dizoo.box2d.lunarlander.envs.lunarlander_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(type='ppo'),
reward_model=dict(type='guided_cost'),
)
lunarlander_ppo_create_config = EasyDict(lunarlander_ppo_create_config)
create_config = lunarlander_ppo_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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