We explore meta-learning agent-agnostic neural Synthetic Environments (SEs) and Reward Networks (RNs) for efficiently training Reinforcement Learning (RL) agents. While an SE acts as a full proxy to a ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results