18 citations · 27 across the 4 of their papers we have counts for
4 papers · 1 filter
Discovering and Achieving Goals via World Models
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis +2
How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision? We decompose this question into two problems: discover…
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling
Russell Mendonca, Xinyang Geng, Chelsea Finn +1
Reinforcement learning algorithms can acquire policies for complex tasks autonomously. However, the number of samples required to learn a diverse set of skills can be prohibitively…
Guided Meta-Policy Search
Russell Mendonca, Abhishek Gupta, Rosen Kralev +3
Reinforcement learning (RL) algorithms have demonstrated promising results on complex tasks, yet often require impractical numbers of samples since they learn from scratch. Meta-RL…
Meta-Reinforcement Learning of Structured Exploration Strategies
Abhishek Gupta, Russell Mendonca, YuXuan Liu +2
Exploration is a fundamental challenge in reinforcement learning (RL). Many of the current exploration methods for deep RL use task-agnostic objectives, such as information gain or…