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from easydict import EasyDict
tabmwp_prompt_pg_config = dict(
exp_name='tabmwp_prompt_pg_seed0',
env=dict(
collector_env_num=1,
evaluator_env_num=1,
n_evaluator_episode=1,
stop_value=1,
cand_number=16,
train_number=80,
engine='text-davinci-002',
temperature=0.,
max_tokens=512,
top_p=1.,
frequency_penalty=0.,
presence_penalty=0.,
option_inds=["A", "B", "C", "D", "E", "F"],
# The API-key of openai. You can get your key in this website: https://platform.openai.com/
api_key='',
enable_replay=True,
prompt_format='TQ-A',
seed=0,
),
policy=dict(
cuda=True,
shot_number=2,
model=dict(
model_name="bert-base-uncased",
add_linear=True,
freeze_encoder=True,
embedding_size=128,
),
learn=dict(
batch_size=10,
# (bool) Whether to normalize advantage. Default to False.
learning_rate=0.001,
# (float) loss weight of the value network, the weight of policy network is set to 1
entropy_weight=0.001,
weight_decay=5e-3,
grad_norm=0.5,
),
collect=dict(
# (int) collect n_sample data, train model 1 times
n_sample=20,
discount_factor=0.,
),
eval=dict(evaluator=dict(eval_freq=500, )),
),
)
main_config = EasyDict(tabmwp_prompt_pg_config)
tabmwp_prompt_pg_config = dict(
env=dict(
type='tabmwp',
import_names=['dizoo.tabmwp.envs.tabmwp_env'],
),
env_manager=dict(type='base'),
policy=dict(type='prompt_pg'),
replay_buffer=dict(type='naive'),
)
create_config = EasyDict(tabmwp_prompt_pg_config)
if __name__ == '__main__':
from ding.entry import serial_pipeline_onpolicy
serial_pipeline_onpolicy((main_config, create_config), seed=0)
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