Aditya Hegde

Dynamic Grid World Navigation using Q-Learning with Moving Obstacles and Terrain-Based Rewards

This paper describes the concept, construction, and experimental assessment of a reinforcement learning (RL) tool for operating in a moving-obstacle and heterogeneous-terrain grid-world environment. The modeled system utilizes tabular Q-Learning, a model-free, off-policy temporal-difference (TD) learning algorithm to t...