Learning Synthetic Environments and Reward Networks for Reinforcement Learning

1 code implementation • ICLR 2022 • Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger, Frank Hutter

In a one-to-one comparison, learning an SE proxy requires more interactions with the real environment than training agents only on the real environment.

reinforcement-learning Reinforcement Learning (RL)