Reinforcement Learning Algorithms: Conducting a Comparative Analysis of Reinforcement Learning Algorithms to Assess Their Strengths and Weaknesses

Authors

  • Rajeev Ranjan 1st Year B.Tech, Computer Science Department, Jodhpur Institute of Engineering & Technology, Jodhpur

Keywords:

Reinforcement Learning, Comparative Study, Q-Learning, SARSA, Deep Q Networks, Policy Gradient, Performance Evaluation, Sample Efficiency, Algorithm Stability, Problem Domain Applicability

Abstract

Reinforcement Learning (RL) has emerged as a powerful paradigm in machine learning, enabling agents to learn optimal behaviors through interaction with environments. Various RL algorithms have been developed, each with unique characteristics and suitability for different applications. This paper presents a comprehensive comparative study of popular RL algorithms, including Q-Learning, SARSA, Deep Q Networks (DQN), Policy Gradient methods, and their variants. We compare these algorithms based on their performance, sample efficiency, stability, and applicability to different problem domains. Through experimental evaluations on standard RL benchmarks, we analyze the strengths and weaknesses of each algorithm, providing insights into their practical implications and areas for improvement.

References

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Published

10-02-2021

How to Cite

[1]
R. Ranjan, “Reinforcement Learning Algorithms: Conducting a Comparative Analysis of Reinforcement Learning Algorithms to Assess Their Strengths and Weaknesses”, J. of Art. Int. Research, vol. 1, no. 1, pp. 1–10, Feb. 2021.

Issue

Vol. 1 No. 1 (2021): Journal of Artificial Intelligence Research

Section

Articles

License

Copyright (c) 2021 Rajeev Ranjan

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