SEQUEL: Semi-Supervised Preference-based RL with Query Synthesis via Latent Interpolation
Daniel Marta,Simon Holk,Christian Pek,Iolanda Leite,Daniel Marta,Simon Holk,Christian Pek,Iolanda Leite
Preference-based reinforcement learning (RL) poses as a recent research direction in robot learning, by allowing humans to teach robots through preferences on pairs of desired behaviours. Nonetheless, to obtain realistic robot policies, an arbitrarily large number of queries is required to be answered by humans. In this work, we approach the sample-efficiency challenge by presenting a technique wh...
