228 citations · 236 across the 2 of their papers we have counts for
3 papers · 1 filter
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?
Kate Rakelly, Abhishek Gupta, Carlos Florensa +1
Mutual information maximization provides an appealing formalism for learning representations of data. In the context of reinforcement learning (RL), such representations can accele…
MELD: Meta-Reinforcement Learning from Images via Latent State Models
Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly +2
Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training ta…
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen +2
Deep reinforcement learning algorithms require large amounts of experience to learn an individual task. While in principle meta-reinforcement learning (meta-RL) algorithms enable a…