Reinforcement Learning for Autonomous Agents
Table Of Contents
<p>1. Introduction<br> 1.1 Background and Significance<br> 1.2 Objectives and Scope of the Project<br>2. Fundamentals of Reinforcement Learning<br> 2.1 Markov Decision Processes<br> 2.2 Value Functions and Policies<br>3. Reinforcement Learning Algorithms<br> 3.1 Q-Learning<br> 3.2 Deep Q Networks (DQN)<br> 3.3 Policy Gradient Methods<br>4. Applications of Reinforcement Learning<br> 4.1 Autonomous Navigation<br> 4.2 Robotic Control<br> 4.3 Resource Allocation<br>5. Training Autonomous Agents<br> 5.1 Simulation Environments<br> 5.2 Reward Design and Function Approximation<br></p>
Thesis Abstract
<p> This project focuses on the application of reinforcement learning techniques to train autonomous agents for decision-making in complex environments. The research will explore various reinforcement learning algorithms and their suitability for training agents to perform tasks such as navigation, control, and optimization. The project aims to investigate the effectiveness of reinforcement learning in enabling autonomous agents to adapt to dynamic and uncertain environments. <br></p>Thesis Overview
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