Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design

4 October 2023

Papers citing "Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design"

8 / 8 papers shown

Title
Can Learned Optimization Make Reinforcement Learning Less Difficult? Alexander David Goldie Chris Xiaoxuan Lu Matthew Jackson Shimon Whiteson Jakob N. Foerster 42 3 0 09 Jul 2024
Discovering Preference Optimization Algorithms with and for Large Language Models Chris Xiaoxuan Lu Samuel Holt Claudio Fanconi Alex J. Chan Jakob Foerster M. Schaar R. T. Lange OffRL 37 15 0 12 Jun 2024
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning Mikayel Samvelyan Akbir Khan Michael Dennis Minqi Jiang Jack Parker-Holder Jakob N. Foerster Roberta Raileanu Tim Rocktaschel 54 24 0 06 Mar 2023
Learning to Optimize for Reinforcement Learning Qingfeng Lan Rupam Mahmood Shuicheng Yan Zhongwen Xu OffRL 26 6 0 03 Feb 2023
Grounding Aleatoric Uncertainty for Unsupervised Environment Design Minqi Jiang Michael Dennis Jack Parker-Holder Andrei Lupu Heinrich Küttler Edward Grefenstette Tim Rocktaschel Jakob N. Foerster 43 13 0 11 Jul 2022
Replay-Guided Adversarial Environment Design Minqi Jiang Michael Dennis Jack Parker-Holder Jakob N. Foerster Edward Grefenstette Tim Rocktaschel 129 95 0 06 Oct 2021
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML Aniruddh Raghu M. Raghu Samy Bengio Oriol Vinyals 177 639 0 19 Sep 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 338 11,684 0 09 Mar 2017