Iterative SARSA: The Modified SARSA Algorithm for Finding the Optimal Path
Prajval Mohan1, Pranav Narayan2, Lakshya Sharma3, Tejas Jambhale4, Simran Koul5
1Prajval Mohan*, B. Tech in Computer Science and Technology from Vellore Institute of Technology, Vellore, India.
2Pranav Narayan, B. Tech in Computer Science and Technology from Vellore Institute of Technology, Vellore, India.
3Lakshya Sharma, B. Tech in Computer Science and Technology from Vellore Institute of Technology, Vellore, India.
4Tejas Jambhale, B. Tech in Computer Science and Technology from Vellore Institute of Technology, Vellore, India.
5Simran Koul B. Tech in Computer Science and Technology from Vellore Institute of Technology, Vellore, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4333-4338 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9429038620/2020©BEIESP | DOI: 10.35940/ijrte.F9429.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a thorough comparative analysis of various reinforcement learning algorithms used by autonomous mobile robots for optimal path finding and, we propose a new algorithm called Iterative SARSA for the same. The main objective of the paper is to differentiate between the Q-learning and SARSA, and modify the latter. These algorithms use either the on-policy or off-policy methods of reinforcement learning. For the on-policy method, we have used the SARSA algorithm and for the off-policy method, the Q-learning algorithm has been used. These algorithms also have an impacting effect on finding the shortest path possible for the robot. Based on the results obtained, we have concluded how our algorithm is better than the current standard reinforcement learning algorithms.
Keywords: Iterative SARSA, Off-policy, On-policy, Optimal path, Q-learning, Reinforcement learning, SARSA
Scope of the Article: Machine/ Deep Learning with IoT & IoE.