Reinforcement Learning by Comparing Immediate Reward

TL;DR

This paper proposes a modified Q-Learning algorithm that compares immediate rewards to improve learning efficiency and reduce episodes needed to reach optimal Q-values in reinforcement learning tasks.

Contribution

It introduces a relative reward-based Q-Learning method that enhances performance by selecting actions with higher immediate rewards, reducing training episodes.

Findings

Faster convergence to optimal Q-values in grid world simulations

Improved performance over standard Q-Learning in deterministic environments

Reduced episodes required for learning optimal policies

Abstract

This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate rewards using a variation of Q-Learning algorithm. Unlike the conventional Q-Learning, the proposed algorithm compares current reward with immediate reward of past move and work accordingly. Relative reward based Q-learning is an approach towards interactive learning. Q-Learning is a model free reinforcement learning method that used to learn the agents. It is observed that under normal circumstances algorithm take more episodes to reach optimal Q-value due to its normal reward or sometime negative reward. In this new form of algorithm agents select only those actions which have a higher immediate reward signal in comparison to previous one. The contribution of this article is the presentation of new Q-Learning Algorithm in order to maximize the performance of algorithm and reduce the number of episode required to reach optimal Q-value. Effectiveness of proposed algorithm is simulated in a 20 x20 Grid world deterministic environment and the result for the two forms of Q-Learning Algorithms is given.