Programmatic policies for interpretable reinforcement learning using pre-trained models
Decision Trees (DTs) are widely used in machine learning due to their critical interpretability. However, training DTs in a Reinforcement Learning (RL) setting is challenging. In the project, we present a framework to improve the interpretability of reinforcement learning (RL) by generating prog...
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Nanyang Technological University
2024
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sg-ntu-dr.10356-1811692024-11-18T01:24:17Z Programmatic policies for interpretable reinforcement learning using pre-trained models Tu, Xia Yang Arvind Easwaran College of Computing and Data Science arvinde@ntu.edu.sg Computer and Information Science LLM Reinforcement learning Decision Trees (DTs) are widely used in machine learning due to their critical interpretability. However, training DTs in a Reinforcement Learning (RL) setting is challenging. In the project, we present a framework to improve the interpretability of reinforcement learning (RL) by generating programmatic policies through large language models (LLMs). Initially looking at decision trees within latent space representation, we proceed to develop and apply a “code reflection” framework in the Karel environment, which provides a controlled setting for evaluating task performance. The framework leverages prompt engineering and code reflection to optimize program synthesis, producing interpretable policies. The report discusses the findings and concludes with recommendations for future work at the end. Bachelor's degree 2024-11-18T01:24:16Z 2024-11-18T01:24:16Z 2024 Final Year Project (FYP) Tu, X. Y. (2024). Programmatic policies for interpretable reinforcement learning using pre-trained models. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181169 https://hdl.handle.net/10356/181169 en SCSE23-0622 application/pdf Nanyang Technological University |
| institution |
Nanyang Technological University |
| building |
NTU Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
NTU Library |
| collection |
DR-NTU |
| language |
English |
| topic |
Computer and Information Science LLM Reinforcement learning |
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Computer and Information Science LLM Reinforcement learning Tu, Xia Yang Programmatic policies for interpretable reinforcement learning using pre-trained models |
| description |
Decision Trees (DTs) are widely used in machine learning due to their critical
interpretability. However, training DTs in a Reinforcement Learning (RL) setting
is challenging.
In the project, we present a framework to improve the interpretability of
reinforcement learning (RL) by generating programmatic policies through large
language models (LLMs). Initially looking at decision trees within latent space
representation, we proceed to develop and apply a “code reflection” framework
in the Karel environment, which provides a controlled setting for evaluating task
performance. The framework leverages prompt engineering and code reflection
to optimize program synthesis, producing interpretable policies.
The report discusses the findings and concludes with recommendations for future
work at the end. |
| author2 |
Arvind Easwaran |
| author_facet |
Arvind Easwaran Tu, Xia Yang |
| format |
Final Year Project |
| author |
Tu, Xia Yang |
| author_sort |
Tu, Xia Yang |
| title |
Programmatic policies for interpretable reinforcement learning using pre-trained models |
| title_short |
Programmatic policies for interpretable reinforcement learning using pre-trained models |
| title_full |
Programmatic policies for interpretable reinforcement learning using pre-trained models |
| title_fullStr |
Programmatic policies for interpretable reinforcement learning using pre-trained models |
| title_full_unstemmed |
Programmatic policies for interpretable reinforcement learning using pre-trained models |
| title_sort |
programmatic policies for interpretable reinforcement learning using pre-trained models |
| publisher |
Nanyang Technological University |
| publishDate |
2024 |
| url |
https://hdl.handle.net/10356/181169 |
| _version_ |
1816859022167375872 |