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Can Learned Optimization Make Reinforcement Learning Less Difficult?

9 July 2024

Alexander David Goldie

Papers citing "Can Learned Optimization Make Reinforcement Learning Less Difficult?"

50 / 64 papers shown

Title
Celo: Training Versatile Learned Optimizers on a Compute Diet A. Moudgil Boris Knyazev Guillaume Lajoie Eugene Belilovsky 427 0 0 23 Jun 2025
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks Michael T. Matthews Michael Beukman Chris Xiaoxuan Lu Jakob Foerster OffRL AI4CE 94 8 0 30 Oct 2024
Simplifying Deep Temporal Difference Learning Matteo Gallici Mattie Fellows Benjamin Ellis B. Pou Ivan Masmitja Jakob Foerster Mario Martin OffRL 126 26 0 05 Jul 2024
Disentangling the Causes of Plasticity Loss in Neural Networks Clare Lyle Zeyu Zheng Khimya Khetarpal H. V. Hasselt Razvan Pascanu James Martens Will Dabney AI4CE 108 37 0 29 Feb 2024
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning Michael T. Matthews Michael Beukman Benjamin Ellis Mikayel Samvelyan Matthew Jackson Samuel Coward Jakob Foerster OffRL 76 31 0 26 Feb 2024
Discovering Temporally-Aware Reinforcement Learning Algorithms Matthew Jackson Chris Xiaoxuan Lu Louis Kirsch R. T. Lange Shimon Whiteson Jakob N. Foerster 102 18 0 08 Feb 2024
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages Guozheng Ma Lu Li Sen Zhang Zixuan Liu Zhen Wang Yixin Chen Li Shen Xueqian Wang Dacheng Tao OffRL 80 20 0 11 Oct 2023
Small batch deep reinforcement learning J. Obando-Ceron Marc G. Bellemare Pablo Samuel Castro VLM 85 19 0 05 Oct 2023
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design Matthew Jackson Minqi Jiang Jack Parker-Holder Risto Vuorio Chris Xiaoxuan Lu Gregory Farquhar Shimon Whiteson Jakob N. Foerster OOD 57 9 0 04 Oct 2023
Resetting the Optimizer in Deep RL: An Empirical Study Kavosh Asadi Rasool Fakoor Shoham Sabach ODL 71 25 0 30 Jun 2023
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks Maxime Chevalier-Boisvert Bolun Dai Mark Towers Rodrigo de Lazcano Lucas Willems Salem Lahlou Suman Pal Pablo Samuel Castro Jordan Terry VGen 98 212 0 24 Jun 2023
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning Hojoon Lee Hanseul Cho Hyunseung Kim Daehoon Gwak Joonkee Kim Jaegul Choo Se-Young Yun Chulhee Yun OffRL 122 29 0 19 Jun 2023
Bigger, Better, Faster: Human-level Atari with human-level efficiency Max Schwarzer J. Obando-Ceron Rameswar Panda Marc G. Bellemare Rishabh Agarwal Pablo Samuel Castro OffRL 91 100 0 30 May 2023
Deep Reinforcement Learning with Plasticity Injection Evgenii Nikishin Junhyuk Oh Georg Ostrovski Clare Lyle Razvan Pascanu Will Dabney André Barreto OffRL 47 52 0 24 May 2023
Massively Scalable Inverse Reinforcement Learning in Google Maps Matt Barnes Matthew Abueg Oliver F. Lange Matt Deeds Jason M. Trader Denali Molitor Markus Wulfmeier S. O’Banion 68 6 0 18 May 2023
Loss of Plasticity in Continual Deep Reinforcement Learning Zaheer Abbas Rosie Zhao Joseph Modayil Adam White Marlos C. Machado CLL OffRL 101 73 0 13 Mar 2023
Understanding plasticity in neural networks Clare Lyle Zeyu Zheng Evgenii Nikishin Bernardo Avila-Pires Razvan Pascanu Will Dabney AI4CE 105 104 0 02 Mar 2023
The Dormant Neuron Phenomenon in Deep Reinforcement Learning Ghada Sokar Rishabh Agarwal Pablo Samuel Castro Utku Evci CLL 94 97 0 24 Feb 2023
Symbolic Discovery of Optimization Algorithms Xiangning Chen Chen Liang Da Huang Esteban Real Kaiyuan Wang ... Xuanyi Dong Thang Luong Cho-Jui Hsieh Yifeng Lu Quoc V. Le 154 377 0 13 Feb 2023
Learning to Optimize for Reinforcement Learning Qingfeng Lan Rupam Mahmood Shuicheng Yan Zhongwen Xu OffRL 79 7 0 03 Feb 2023
evosax: JAX-based Evolution Strategies R. T. Lange 100 57 0 08 Dec 2022
Transformer-Based Learned Optimization Erik Gartner Luke Metz Mykhaylo Andriluka C. Freeman C. Sminchisescu 75 11 0 02 Dec 2022
VeLO: Training Versatile Learned Optimizers by Scaling Up Luke Metz James Harrison C. Freeman Amil Merchant Lucas Beyer ... Naman Agrawal Ben Poole Igor Mordatch Adam Roberts Jascha Narain Sohl-Dickstein 106 60 0 17 Nov 2022
Discovered Policy Optimisation Chris Xiaoxuan Lu J. Kuba Alistair Letcher Luke Metz Christian Schroeder de Witt Jakob N. Foerster OffRL 72 79 0 11 Oct 2022
The Primacy Bias in Deep Reinforcement Learning Evgenii Nikishin Max Schwarzer P. DÓro Pierre-Luc Bacon Rameswar Panda OnRL 146 194 0 16 May 2022
Understanding and Preventing Capacity Loss in Reinforcement Learning Clare Lyle Mark Rowland Will Dabney CLL 84 113 0 20 Apr 2022
Practical tradeoffs between memory, compute, and performance in learned optimizers Luke Metz C. Freeman James Harrison Niru Maheswaranathan Jascha Narain Sohl-Dickstein 138 32 0 22 Mar 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 886 13,176 0 04 Mar 2022
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies Paul Vicol Luke Metz Jascha Narain Sohl-Dickstein 91 68 0 27 Dec 2021
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning Robert Kirk Amy Zhang Edward Grefenstette Tim Rocktaschel OffRL 87 170 0 18 Nov 2021
Benchmarking the Spectrum of Agent Capabilities Danijar Hafner ELM 86 140 0 14 Sep 2021
Deep Reinforcement Learning at the Edge of the Statistical Precipice Rishabh Agarwal Max Schwarzer Pablo Samuel Castro Aaron Courville Marc G. Bellemare OffRL 123 676 0 30 Aug 2021
Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation C. Freeman Erik Frey Anton Raichuk Sertan Girgin Igor Mordatch Olivier Bachem 108 380 0 24 Jun 2021
Correcting Momentum in Temporal Difference Learning Emmanuel Bengio Joelle Pineau Doina Precup 34 10 0 07 Jun 2021
A Generalizable Approach to Learning Optimizers Diogo Almeida Clemens Winter Jie Tang Wojciech Zaremba AI4CE 82 29 0 02 Jun 2021
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design Michael Dennis Natasha Jaques Eugene Vinitsky Alexandre M. Bayen Stuart J. Russell Andrew Critch Sergey Levine 83 236 0 03 Dec 2020
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves Luke Metz Niru Maheswaranathan C. Freeman Ben Poole Jascha Narain Sohl-Dickstein 144 61 0 23 Sep 2020
Discovering Reinforcement Learning Algorithms Junhyuk Oh Matteo Hessel Wojciech M. Czarnecki Zhongwen Xu H. V. Hasselt Satinder Singh David Silver 67 129 0 17 Jul 2020
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning Maximilian Igl Gregory Farquhar Jelena Luketina Wendelin Boehmer Shimon Whiteson 81 88 0 10 Jun 2020
Improving Generalization in Meta Reinforcement Learning using Learned Objectives Louis Kirsch Sjoerd van Steenkiste Jürgen Schmidhuber OffRL 89 119 0 09 Oct 2019
Behaviour Suite for Reinforcement Learning Ian Osband Yotam Doron Matteo Hessel John Aslanides Eren Sezener ... Satinder Singh Benjamin Van Roy R. Sutton David Silver H. V. Hasselt OffRL 92 183 0 09 Aug 2019
On the Convergence of Adam and Beyond Sashank J. Reddi Satyen Kale Surinder Kumar 106 2,505 0 19 Apr 2019
Understanding and correcting pathologies in the training of learned optimizers Luke Metz Niru Maheswaranathan Jeremy Nixon C. Freeman Jascha Narain Sohl-Dickstein ODL 79 147 0 24 Oct 2018
Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods Peter Henderson Joshua Romoff Joelle Pineau 67 34 0 05 Oct 2018
Meta-Learning Update Rules for Unsupervised Representation Learning Luke Metz Niru Maheswaranathan Brian Cheung Jascha Narain Sohl-Dickstein SSL OOD 77 123 0 31 Mar 2018
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor Tuomas Haarnoja Aurick Zhou Pieter Abbeel Sergey Levine 317 8,406 0 04 Jan 2018
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning F. Such Vashisht Madhavan Edoardo Conti Joel Lehman Kenneth O. Stanley Jeff Clune 111 693 0 18 Dec 2017
Proximal Policy Optimization Algorithms John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov OffRL 535 19,265 0 20 Jul 2017
Deep reinforcement learning from human preferences Paul Christiano Jan Leike Tom B. Brown Miljan Martic Shane Legg Dario Amodei 218 3,365 0 12 Jun 2017
Parameter Space Noise for Exploration Matthias Plappert Rein Houthooft Prafulla Dhariwal Szymon Sidor Richard Y. Chen Xi Chen Tamim Asfour Pieter Abbeel Marcin Andrychowicz 73 597 0 06 Jun 2017