Trajectory Data to Improve Unsupervised Learning and Intrinsic


  • Laxmi Gautam P.G. Student, Department of Computer Science, Walchand Institute of Technology (W.I.T), Solapur, Maharashtra, India
  • Rajneesh Kumar P.G. Student, Department of Computer Science, Walchand Institute of Technology (W.I.T), Solapur, Maharashtra, India



trajectory data, learning, motivation


The three primary components of machine learning (ML) are reinforcement learning, unstructured learning, and structured learning. The last level, reinforcement learning, will be the main topic of this study. We'll cover a few of the more well-liked reinforcement learning techniques, though there are many more. Reinforcement agents are software agents that make use of reinforcement learning to optimize their rewards within a specific context. The two primary categories of rewards are extrinsic and intrinsic. It's a certain result we obtain after abiding by a set of guidelines and achieving a particular objective. An even better illustration of an intrinsic reward than money is the agent's enthusiasm to learn new skills that could come in handy later on.


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How to Cite

Laxmi Gautam, & Rajneesh Kumar. (2024). Trajectory Data to Improve Unsupervised Learning and Intrinsic. Applied Science and Biotechnology Journal for Advanced Research, 3(1), 16–20.