22 citations · 76 across the 14 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
cs.LG2021★ 2 cited
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Deep reinforcement learning (RL) agents that exist in high-dimensional state spaces, such as those composed of images, have interconnected learning burdens. Agents must learn an ac…
cs.LG2021★ 5 cited
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
Lukas Schäfer, Filippos Christianos, Josiah P. Hanna +1
Intrinsic rewards can improve exploration in reinforcement learning, but the exploration process may suffer from instability caused by non-stationary reward shaping and strong depe…