Learning Efficient Navigation in a Minimal-Input Reinforcement Learning Agent

This project presents the design and implementation of a Reinforcement Learning (RL) agent trained to learn optimal behavior through interaction with an environment. The agent is developed using a Q-learning / neural network-based approach (depending on implementation) and improves its performance over time by maximizing cumulative rewards. The system explores how an agent can learn decision-making strategies without explicit programming of rules, relying instead on reward feedback. The project includes the training pipeline, environment setup, and evaluation of the agent’s performance over multiple episodes. The implementation is available on GitHub, along with source code and experimental results. This work demonstrates fundamental concepts of reinforcement learning, including exploration vs exploitation, reward optimization, and policy improvement.