Dynamic Kernel Learning for Adaptive Reinforcement Learning

Abstract

This paper presents a novel reinforcement learning (RL) algorithm, Dynamic Kernel Learning (DKL), designed to enhance the performance of RL agents in complex environments. DKL addresses the limitations of traditional RL methods that often rely on fixed kernel functions for value estimation. The core innovation lies in the continuous adaptation of the kernel function itself through a meta-learning approach. A dedicated neural network learns to adjust the kernel parameters, such as the bandwidth of a Gaussian kernel, based on the agent's reward signals and state transitions. This dynamic adaptation allows the agent to effectively generalize across diverse states and improve exploration efficiency. We demonstrate the effectiveness of DKL through theoretical analysis and outline its key components and training procedure. The algorithm offers a promising direction for improving the robustness and adaptability of RL agents, particularly in scenarios with high-dimensional state spaces and non-stationary environments. The key benefit is the ability to tailor the value function representation to the current state of the environment, leading to faster convergence and better final performance.