439 citations · 1.3k across the 23 of their papers we have counts for
24 papers
Deep Reinforcement Learning with Plasticity Injection
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski +4
A growing body of evidence suggests that neural networks employed in deep reinforcement learning (RL) gradually lose their plasticity, the ability to learn from new data; however,…
In-context Reinforcement Learning with Algorithm Distillation
Michael Laskin, Luyu Wang, Junhyuk Oh +11
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal seque…
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity
Marta Garnelo, Wojciech Marian Czarnecki, Siqi Liu +5
Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of…
Introducing Symmetries to Black Box Meta Reinforcement Learning
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt +3
Meta reinforcement learning (RL) attempts to discover new RL algorithms automatically from environment interaction. In so-called black-box approaches, the policy and the learning a…
Self-Imitation Learning by Planning
Sha Luo, Hamidreza Kasaei, Lambert Schomaker
Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge, which is widely adopted in reinforcement learning (RL) to initialize exploration.…
Discovery of Options via Meta-Learned Subgoals
Vivek Veeriah, Tom Zahavy, Matteo Hessel +6
Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster. However, despite prior work on this topic, the problem of disc…