The focus of my Master’s research was on evolutionary reinforcement learning. In particular, scaling neuroevolution to reinforcement learning problems with high-dimensional inputs (e.g. images). I was motivated by the fact that recent success in solving hard reinforcement learning problems can be largely credited to the use of deep neural networks, which can extract high-level features and learn compact state representations from high-dimensional inputs, such as images. However, the large networks required to learn both state representation and policy using this approach limit the effectiveness and benefits of neuroevolution methods that have proven effective at solving simpler problems in the past. We pursued a solution to this problem that separates state representation and policy learning and only apply neuroevolution to the latter.
- IVCNZEvaluating Learned State Representations for AtariIn 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), Nov 2020
- GECCOEvolving Neural Network Agents to Play Atari Games with Compact State RepresentationsIn Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, Nov 2020
- Evolutionary Reinforcement Learning for Vision-Based General Video Game PlayingUniversity of Canterbury, Aug 2020