Title: Adaptive Reinforcement Learning Reward Adjustment

Abstract

This paper investigates a novel reinforcement learning algorithm utilizing adaptive reward function adjustment to enhance learning performance. Traditional reinforcement learning often relies on fixed reward functions, which can struggle to adapt to complex environments and unexpected situations. We propose a method that dynamically adjusts the reward function based on the learning process's inherent noise and uncertainty. This is achieved through a novel "adaptive adjustment" mechanism that continuously monitors and modifies the reward function in response to these factors. The core mechanism aims to mitigate the limitations of static reward functions, leading to improved sample efficiency and robustness in reinforcement learning. This work addresses the critical gap in current approaches by introducing a mechanism to dynamically tune the reward landscape, thereby improving the generalization capabilities of reinforcement learning agents.