Symbolic Regression for Interpretable Offline Reinforcement Learning
Bhairav Mehta, Max Tegmark
Submitted
[Dataset: Coming Soon][Arxiv: Coming Soon]

While recent years have been company to much progress in the reinforcement learning community, many tasks in use today still rely on carefully designed reward functions, many of which are products of constant tweaking and tuning by engineers and scientists. These reward functions, often dense, symbolic functions of state, don't exist in real world datasets, many of which are labeled by human experimenters - each with their own biases about desired behavior. In this work, we describe a new paradigm of extracting symbolic reward functions from noisy data called Interpretable Symbolic Reinforcement Learning (ISRL). ISRL allows for human experimenters to extract interpretable reward functions solely from data via symbolic regression.