Evolutionary Reinforcement Learning for Vision-Based General Video Game Playing
University of Canterbury
Time & Place
Mon, 21 Sep 2020 15:00:15 NZST in E16 - Engineering Core
All are welcome
Over the past decade, video games have become increasingly utilised for research in artificial intelligence. Perhaps the most extensive use of video games has been as benchmark problems in the field of reinforcement learning. Part of the reason for this is because video games are designed to challenge humans, and as a result, developing methods capable of mastering them is considered a stepping stone to achieving human-level performance in real-world tasks. Of particular interest are vision-based general video game playing (GVGP) methods. These are methods that learn from pixel inputs and can be applied, without modification, across sets of games. One of the challenges in evolutionary computing is scaling up neuroevolution methods, which have proven effective at solving simpler reinforcement learning problems in the past, to tasks with high-dimensional input spaces, such as video games. In this seminar, I will present our work on developing a novel method for vision-based GVGP that combines the representational learning power of deep neural networks and the policy learning benefits of neuroevolution. This is achieved by separating state representation and policy learning and applying neuroevolution only to the latter.