Cognitive Reinforcement Learning: Integrating PPO, Hyperdimensional Computing and Neuro-Symbolic AI with Vector Symbolic Architectures
Introduction
Reinforcement Learning (RL) has become a key player in solving complex decision-making tasks, ranging from game-playing agents to robotics and autonomous systems. Among the array of RL algorithms out there Proximal Policy Optimization (PPO) stands out as an efficient and stable approach to policy optimization.
However, when dealing with high-dimensional state spaces or tasks that require symbolic reasoning standard RL methods may fall substantially short. By integrating Hyperdimensional Computing (HDC) and Neuro-Symbolic Processes we can empower RL agents to tackle high-dimensional problems and inject prior knowledge to improve learning efficiency.
In this in-depth article, we will walk through an advanced PPO agent that incorporates HDC for high-dimensional state representation and Neuro-Symbolic Integration to guide decision-making. We’ll break down the mathematics, explain the role of each component, and provide detailed code snippets to demonstrate how this hybrid approach can be implemented.
