DefaultVR: the AI Expansion. An application of artificial intelligence in competitive gaming and virtual reality.

This works presents the development of DefaultVR: The AI Expansion, an expansion of the first degree thesis DefaultVR, a virtual reality tactical shooter online game. In particular, this expansion aims to include an artificial agent, called Eve, capable of learning to play in the virtual reality world through reinforcement learning techniques. The agent learns to navigate the game environment, make decisions based on pseudo-visual information, and optimize its actions to maximize rewards. The development utilizes a deep reinforcement learning framework with the Proximal Policy Optimization algorithm included with Units’s ML-Agents. Extensive experiments were conducted to evaluate the agent’s performance, comparing it against itself and human players. The results demonstrate the agent’s ability to adapt and improve over time, achieving competitive gameplay skills comparable to both new and experienced human VR players. The training process involved iterative optimization and analysis of various hyperparameters, observations’ and actions’ spaces, and training configurations. The successful development of the artificial agent has significant implications for the field of gaming AI, showcasing its potential for creating engaging and challenging gameplay experiences. The research contributes to the broader understanding of reinforcement learning techniques and their application in training intelligent agents for real-world tasks.