Evaluating the effects of hyperparameter optimization in VizDoom

Reinforcement learning is a machine learning technique in which an artificial intelligence agent is guided by positive and negative rewards to learn strategies. To guide the agent’s behavior in addition to the reward are its hyperparameters. These values control how the agent learns. These hyperparameters are rarely disclosed in contemporary research, making it hard to estimate the value of optimizing these hyperparameters. This study aims to partly compare three different popular reinforcement learning algorithms, Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C) and Deep Q Network (DQN), and partly investigate the effects of hyperparameter optimization of several hyperparameters for each algorithm. All the included algorithms showed a significant difference after hyperparameter optimization, resulting in higher performance. A2C showed the largest performance increase after hyperparameter optimization, and PPO performed the best of the three algorithms both with default and optimized hyperparameters