Using Rotation, Translation, and Cropping to Boost Generalization in Deep Reinforcement Learning…

"Generalization" is an AI buzzword these days for good reason: most scientists would love to see the models they're training in simulations and video game environments evolve and expand to take on meaningful real-world challenges -- for example in safety, conservation, medicine, etc. One concerned research area is deep reinforcement learning (DRL), which implements deep learning architectures with reinforcement learning algorithms to enable AI agents to learn the best actions possible to attain their goals in virtual environments. DRL has been widely applied in games and robotics. Such DRL agents have an impressive track record on Starcraft II and Dota-2. But because they were trained in fixed environments, studies suggest DRL agents can fail to generalize to even slight variations of their training environments. In a new paper, researchers from the New York University and Modl.ai, a company applying machine learning to game developing, suggest that simple spacial processing methods such as rotation, translation and cropping could help increase model generality.