This group project highlights the effectiveness of utilizing reinforcement learning (RL) along
with the Proximal Policy Optimization (PPO) algorithm to train an agent to play aWolfenstein3Dlike
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.