Reinforcement Learning by Comparing Immediate Reward

Details Reinforcement Learning by Comparing Immediate Reward Reinforcement Learning by Comparing Immediate Reward

2010-09-14

arxiv.org/abs/1009.2566v1

Computer Science

Learning

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Reinforcement Learning by Comparing Immediate Reward does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Reinforcement Learning by Comparing Immediate Reward, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Reinforcement Learning by Comparing Immediate Reward will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-395071