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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) | |