  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.
