2022
Learning Synthetic Environments and Reward Networks for Reinforcement Learning
ICLR 2022poster
We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We show that an agent, after being trained exclusively on the SE, is able to solve the corresponding real environment. Whi…