Programmatically Interpretable Reinforcement Learning
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Updated
May 20, 2021 - Python
Programmatically Interpretable Reinforcement Learning
A comprehensive implementation of a Neurosymbolic framework for Visual Question Answering (VQA) on the CLEVR dataset. This project translates natural language questions into symbolic programs using three different learning strategies: Supervised (LSTM & Transformer), Reinforcement Learning (REINFORCE), and In-Context Learning (LLM).
CLI tool that generates Python programs with OpenAI, saves them to a file, runs them, and retries up to 5 times with error-fixing.
DPG: A DeLP program generator
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