AutoML | Learning Synthetic Environments and Reward Networks for Reinforcement Learning

Learning Synthetic Environments and Reward Networks for Reinforcement Learning

In supervised learning, multiple works have investigated training networks using artificial data. For instance, in dataset distillation, the information of a larger dataset is distilled into a smaller synthetic dataset in order to improve train time. Synthetic environments (SEs) aim to apply a similar idea to Reinforcement learning (RL). They are proxies for real environments […]