AUGMENTING SELF-LEARNING AGENT IN FIRST-PERSON SHOOTER GAME USING REINFORCEMENT LEARNING

Abstract

This group project highlights the effectiveness of utilizing reinforcement learning (RL) alongwith the Proximal Policy Optimization (PPO) algorithm to train an agent to play Wolfenste in3D like game with multiple levels. The agent exhibited exceptional performance in relation to reward, time efficiency, and overall effectiveness. An in-depth analysis of its performance indicated marked enhancements in the reward curves, strategic navigation throughout the game levels, and expeditious completion of each level. The study highlights the potential of RL and PPO for training agents in complex video games with multiple levels, as well as in other applications such as agent-based modeling and machine learning.