Hands-On Reinforcement Learning for Games

Why RL in games?

Various forms of machine learning systems have been used in gaming, with supervised learning being the primary choice. While these methods can be made to look intelligent, they are still limited by working on labeled or categorized data. While generative adversarial networks (GANs) show a particular promise in level and other asset generation, these families of algorithms cannot plan and make sense of long-term decision making. AI systems that replicate planning and interactive behavior in games are now typically done with hardcoded state machine systems such as finite state machines or behavior trees. Being able to develop agents that can learn for themselves the best moves or actions for an environment is literally game-changing, ...