Large-Scale Retrieval for Reinforcement Learning

no code implementations • 10 Jun 2022 • Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent SIfre, Théophane Weber, Timothy Lillicrap

Effective decision making involves flexibly relating past experiences and relevant contextual information to a novel situation.

Decision Making Offline RL +4

Retrieval-Augmented Reinforcement Learning

no code implementations • 17 Feb 2022 • Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adria Puigdomenech Badia, Arthur Guez, Mehdi Mirza, Peter C. Humphreys, Ksenia Konyushkova, Laurent SIfre, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, Charles Blundell

In this paper we explore an alternative paradigm in which we train a network to map a dataset of past experiences to optimal behavior.

reinforcement-learning Reinforcement Learning +2

Deep Learning without Weight Transport

3 code implementations • NeurIPS 2019 • Mohamed Akrout, Collin Wilson, Peter C. Humphreys, Timothy Lillicrap, Douglas Tweed

Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedback path, in a way that is likely impossible biologically.