Topic: Autonomous Navigation of a Mobile Robot using Deep Reinforcement Learning. Objective: develop ...

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Topic: Autonomous Navigation of a Mobile Robot using Deep Reinforcement Learning. Objective: develop an RL-based autonomous navigation system for a mobile robot that uses deep RL algorithms to learn the optimal control policy for navigating an unknown environment, exploring the applicability of deep RL to robotics and evaluating the performance of different RL algorithms for mobile robot navigation. Related work: prior work has shown the effectiveness of RL algorithms for robot control and navigation, but most approaches rely on simple environments and do not generalize well to complex, unknown ones; many also require extensive expert knowledge in reward function design and feature engineering, which limits their scalability. Technical outline: use the ROS (Robot Operating System) environment and the TurtleBot3 platform; base the approach on deep Q-learning (DQL) and policy gradient (PG) algorithms to learn a navigation policy in a maze-like environment; the novelty is integrating these algorithms into a hybrid approach that can learn effectively in complex, unknown environments, and exploring how to incorporate sensory feedback from cameras and lidars. Implement the system. You may split it into two parts: Part I, build the environment and make the robot understand it; Part II, implement the full code, with code complexity increasing across the parts. Describe the environment and the code at the end.