Reinforcement Learning with PyReason as a Semantic Proxy

Explore a technical talk on using PyReason as a semantic proxy for a simulator in reinforcement learning applications. Delve into the research presented by Kuastuv Mukherji from Arizona State University at IEEE ICSC '24. Learn about the innovative approach of combining symbolic methods with deep learning techniques in this 17-minute presentation. Discover how PyReason, a Python package for neuro-symbolic AI, can be leveraged to enhance reinforcement learning processes. Access the preprint of the paper on arXiv for in-depth insights. Gain valuable knowledge about the intersection of logic programming and machine learning, contributing to advancements in artificial general intelligence (AGI). For those interested in further exploration, find additional resources and information about PyReason on the Neuro Symbolic website.

Reinforcement Learning with PyReason as a Semantic Proxy (Kuastuv Mukherji, ASU)