Effects of Self-Driven Reinforcement Learning


  • Sanjay Rao P.G. Student, Department of Engineering, Walchand Institute of Technology (W.I.T), Solapur, Maharashtra, India
  • Rohit Bharat P.G. Student, Department of Engineering, Walchand Institute of Technology (W.I.T), Solapur, Maharashtra, India


reinforcement learning, structure, limitations, agents


Structured Learning, unstructured Learning, and reinforcement Learning is the three main components of machine Learning (ML). In this paper, we'll focus on reinforcement Learning, which is the final stage. There are numerous methods of reinforcement learning, and we'll go over some of the more popular ones. Software agents that use reinforcement learning to maximize their rewards in a given environment are known as reinforcement agents. Extrinsic and intrinsic rewards are the two main classifications of rewards. It's a specific outcome we get after following a set of rules and accomplishing a specific goal. Rather than monetary gain, a better example of an intrinsic reward is the agent's eagerness to learn newly acquired expertise that may prove beneficial in the future.


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How to Cite

Sanjay Rao, & Rohit Bharat. (2022). Effects of Self-Driven Reinforcement Learning. Applied Science and Biotechnology Journal for Advanced Research, 1(1), 20–24. Retrieved from https://abjar.vandanapublications.com/index.php/ojs/article/view/5