Modeling adversary behavior using Q-Learning

This project explores the application of reinforcement learning and game theory principles in modeling strategic interactions within a conflict scenario. A Q-learning algorithm is developed to optimize decision-making by learning from rewards and risks associated with different actions. The analysis involves tracking the learning progress, evaluating the effectiveness of learned policies, and testing the algorithm’s performance under varying consequences. Results indicate that incorporating risk values into the Q-learning process enhances decision quality. Next, game theory is employed to create a strategic interaction between an agent and an adversary. The agent strategically manipulates the adversary’s decisions by altering risk values associated with potential actions. Findings demonstrate the agent’s ability to influence the adversary’s choices, reduce their cumulative reward, and introduce delays in the adversary’s decision-making. This experiment sheds light on the dynamic interplay between reinforcement learning and strategic manipulation, offering insights into decision-making processes within complex environments.