Timezone: »
Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction. It addresses challenges with regard to the cost of data collection and safety, both of which are particularly pertinent to real-world applications of RL. Unfortunately, most off-policy algorithms perform poorly when learning from a fixed dataset. In this paper, we propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR). We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces -- outperforming several state-of-the-art offline RL algorithms by a significant margin on a wide range of benchmark tasks.
Author Information
Ziyu Wang (Google Brain)
Alexander Novikov (DeepMind)
Konrad Zolna (DeepMind)
Josh Merel (DeepMind)
Jost Tobias Springenberg (DeepMind)
Scott Reed (Google DeepMind)
Bobak Shahriari (Deepmind)
Noah Siegel (DeepMind)
Caglar Gulcehre (DeepMind)
Nicolas Heess (Google DeepMind)
Nando de Freitas (DeepMind)
More from the Same Authors
-
2021 : Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration »
Oliver Groth · Markus Wulfmeier · Giulia Vezzani · Vibhavari Dasagi · Tim Hertweck · Roland Hafner · Nicolas Heess · Martin Riedmiller -
2021 : Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies »
Dushyant Rao · Fereshteh Sadeghi · Leonard Hasenclever · Markus Wulfmeier · Martina Zambelli · Giulia Vezzani · Dhruva Tirumala · Yusuf Aytar · Josh Merel · Nicolas Heess · Raia Hadsell -
2021 : Offline Meta-Reinforcement Learning for Industrial Insertion »
Tony Zhao · Jianlan Luo · Oleg Sushkov · Rugile Pevceviciute · Nicolas Heess · Jonathan Scholz · Stefan Schaal · Sergey Levine -
2022 : Solving Math Word Problems with Process-based and Outcome-based Feedback »
Jonathan Uesato · Nate Kushman · Ramana Kumar · H. Francis Song · Noah Siegel · Lisa Wang · Antonia Creswell · Geoffrey Irving · Irina Higgins -
2022 : Multi-step Planning for Automated Hyperparameter Optimization with OptFormer »
Lucio M Dery · Abram Friesen · Nando de Freitas · Marc'Aurelio Ranzato · Yutian Chen -
2022 : Solving Math Word Problems with Process-based and Outcome-based Feedback »
Jonathan Uesato · Nate Kushman · Ramana Kumar · H. Francis Song · Noah Siegel · Lisa Wang · Antonia Creswell · Geoffrey Irving · Irina Higgins -
2022 : Panel RL Benchmarks »
Minmin Chen · Pablo Samuel Castro · Caglar Gulcehre · Tony Jebara · Peter Stone -
2022 Poster: Towards Learning Universal Hyperparameter Optimizers with Transformers »
Yutian Chen · Xingyou Song · Chansoo Lee · Zi Wang · Richard Zhang · David Dohan · Kazuya Kawakami · Greg Kochanski · Arnaud Doucet · Marc'Aurelio Ranzato · Sagi Perel · Nando de Freitas -
2022 Poster: Data augmentation for efficient learning from parametric experts »
Alexandre Galashov · Josh Merel · Nicolas Heess -
2021 : Retrospective Panel »
Sergey Levine · Nando de Freitas · Emma Brunskill · Finale Doshi-Velez · Nan Jiang · Rishabh Agarwal -
2021 Poster: Entropic Desired Dynamics for Intrinsic Control »
Steven Hansen · Guillaume Desjardins · Kate Baumli · David Warde-Farley · Nicolas Heess · Simon Osindero · Volodymyr Mnih -
2021 Poster: Neural Production Systems »
Anirudh Goyal · Aniket Didolkar · Nan Rosemary Ke · Charles Blundell · Philippe Beaudoin · Nicolas Heess · Michael Mozer · Yoshua Bengio -
2020 : Panel »
Emma Brunskill · Nan Jiang · Nando de Freitas · Finale Doshi-Velez · Sergey Levine · John Langford · Lihong Li · George Tucker · Rishabh Agarwal · Aviral Kumar -
2020 : Mini-panel discussion 1 - Bridging the gap between theory and practice »
Aviv Tamar · Emma Brunskill · Jost Tobias Springenberg · Omer Gottesman · Daniel Mankowitz -
2020 : Contributed Talk 4: Addressing Extrapolation Error in Deep Offline Reinforcement Learning »
Caglar Gulcehre -
2020 : Keynote: Jost Tobias Springenberg »
Jost Tobias Springenberg -
2020 : Offline RL »
Nando de Freitas -
2020 Poster: Value-driven Hindsight Modelling »
Arthur Guez · Fabio Viola · Theophane Weber · Lars Buesing · Steven Kapturowski · Doina Precup · David Silver · Nicolas Heess -
2020 Poster: Modular Meta-Learning with Shrinkage »
Yutian Chen · Abram Friesen · Feryal Behbahani · Arnaud Doucet · David Budden · Matthew Hoffman · Nando de Freitas -
2020 Spotlight: Modular Meta-Learning with Shrinkage »
Yutian Chen · Abram Friesen · Feryal Behbahani · Arnaud Doucet · David Budden · Matthew Hoffman · Nando de Freitas -
2020 Poster: RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning »
Caglar Gulcehre · Ziyu Wang · Alexander Novikov · Thomas Paine · Sergio Gómez · Konrad Zolna · Rishabh Agarwal · Josh Merel · Daniel Mankowitz · Cosmin Paduraru · Gabriel Dulac-Arnold · Jerry Li · Mohammad Norouzi · Matthew Hoffman · Nicolas Heess · Nando de Freitas -
2020 Poster: Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces »
Guy Lorberbom · Chris Maddison · Nicolas Heess · Tamir Hazan · Danny Tarlow -
2020 Poster: Training Generative Adversarial Networks by Solving Ordinary Differential Equations »
Chongli Qin · Yan Wu · Jost Tobias Springenberg · Andy Brock · Jeff Donahue · Timothy Lillicrap · Pushmeet Kohli -
2020 Spotlight: Training Generative Adversarial Networks by Solving Ordinary Differential Equations »
Chongli Qin · Yan Wu · Jost Tobias Springenberg · Andy Brock · Jeff Donahue · Timothy Lillicrap · Pushmeet Kohli -
2019 Workshop: Science meets Engineering of Deep Learning »
Levent Sagun · Caglar Gulcehre · Adriana Romero Soriano · Negar Rostamzadeh · Nando de Freitas -
2019 : Welcoming remarks and introduction »
Levent Sagun · Caglar Gulcehre · Adriana Romero Soriano · Negar Rostamzadeh · Nando de Freitas -
2019 Poster: Hindsight Credit Assignment »
Anna Harutyunyan · Will Dabney · Thomas Mesnard · Mohammad Gheshlaghi Azar · Bilal Piot · Nicolas Heess · Hado van Hasselt · Gregory Wayne · Satinder Singh · Doina Precup · Remi Munos -
2019 Poster: Learning Compositional Neural Programs with Recursive Tree Search and Planning »
Thomas PIERROT · Guillaume Ligner · Scott Reed · Olivier Sigaud · Nicolas Perrin · Alexandre Laterre · David Kas · Karim Beguir · Nando de Freitas -
2019 Spotlight: Hindsight Credit Assignment »
Anna Harutyunyan · Will Dabney · Thomas Mesnard · Mohammad Gheshlaghi Azar · Bilal Piot · Nicolas Heess · Hado van Hasselt · Gregory Wayne · Satinder Singh · Doina Precup · Remi Munos -
2019 Spotlight: Learning Compositional Neural Programs with Recursive Tree Search and Planning »
Thomas PIERROT · Guillaume Ligner · Scott Reed · Olivier Sigaud · Nicolas Perrin · Alexandre Laterre · David Kas · Karim Beguir · Nando de Freitas -
2018 : Discussion Panel: Ryan Adams, Nicolas Heess, Leslie Kaelbling, Shie Mannor, Emo Todorov (moderator: Roy Fox) »
Ryan Adams · Nicolas Heess · Leslie Kaelbling · Shie Mannor · Emo Todorov · Roy Fox -
2018 : Probabilistic Reasoning for Reinforcement Learning (Nicolas Heess) »
Nicolas Heess -
2018 : TBA 5 »
Nando de Freitas -
2018 : Invited Talk 5: Nando de Freitas »
Nando de Freitas -
2018 Poster: Playing hard exploration games by watching YouTube »
Yusuf Aytar · Tobias Pfaff · David Budden · Thomas Paine · Ziyu Wang · Nando de Freitas -
2018 Spotlight: Playing hard exploration games by watching YouTube »
Yusuf Aytar · Tobias Pfaff · David Budden · Thomas Paine · Ziyu Wang · Nando de Freitas -
2018 Poster: Neural Arithmetic Logic Units »
Andrew Trask · Felix Hill · Scott Reed · Jack Rae · Chris Dyer · Phil Blunsom -
2017 Poster: Distral: Robust multitask reinforcement learning »
Yee Teh · Victor Bapst · Wojciech Czarnecki · John Quan · James Kirkpatrick · Raia Hadsell · Nicolas Heess · Razvan Pascanu -
2017 Poster: Imagination-Augmented Agents for Deep Reinforcement Learning »
Sébastien Racanière · Theophane Weber · David Reichert · Lars Buesing · Arthur Guez · Danilo Jimenez Rezende · Adrià Puigdomènech Badia · Oriol Vinyals · Nicolas Heess · Yujia Li · Razvan Pascanu · Peter Battaglia · Demis Hassabis · David Silver · Daan Wierstra -
2017 Oral: Imagination-Augmented Agents for Deep Reinforcement Learning »
Sébastien Racanière · Theophane Weber · David Reichert · Lars Buesing · Arthur Guez · Danilo Jimenez Rezende · Adrià Puigdomènech Badia · Oriol Vinyals · Nicolas Heess · Yujia Li · Razvan Pascanu · Peter Battaglia · Demis Hassabis · David Silver · Daan Wierstra -
2017 Poster: Filtering Variational Objectives »
Chris Maddison · John Lawson · George Tucker · Nicolas Heess · Mohammad Norouzi · Andriy Mnih · Arnaud Doucet · Yee Teh -
2017 Poster: Robust Imitation of Diverse Behaviors »
Ziyu Wang · Josh Merel · Scott Reed · Nando de Freitas · Gregory Wayne · Nicolas Heess -
2017 Poster: Learning Hierarchical Information Flow with Recurrent Neural Modules »
Danijar Hafner · Alexander Irpan · James Davidson · Nicolas Heess -
2017 Tutorial: Deep Learning: Practice and Trends »
Nando de Freitas · Scott Reed · Oriol Vinyals -
2016 Workshop: Neural Abstract Machines & Program Induction »
Matko Bošnjak · Nando de Freitas · Tejas Kulkarni · Arvind Neelakantan · Scott E Reed · Sebastian Riedel · Tim Rocktäschel -
2016 : Nando De Freitas »
Nando de Freitas -
2016 : Learning To Optimize »
Nando de Freitas -
2016 Poster: Unsupervised Learning of 3D Structure from Images »
Danilo Jimenez Rezende · S. M. Ali Eslami · Shakir Mohamed · Peter Battaglia · Max Jaderberg · Nicolas Heess -
2016 Poster: Attend, Infer, Repeat: Fast Scene Understanding with Generative Models »
S. M. Ali Eslami · Nicolas Heess · Theophane Weber · Yuval Tassa · David Szepesvari · koray kavukcuoglu · Geoffrey E Hinton -
2016 Poster: Learning to learn by gradient descent by gradient descent »
Marcin Andrychowicz · Misha Denil · Sergio Gómez · Matthew Hoffman · David Pfau · Tom Schaul · Nando de Freitas -
2015 Workshop: Bayesian Optimization: Scalability and Flexibility »
Bobak Shahriari · Ryan Adams · Nando de Freitas · Amar Shah · Roberto Calandra -
2015 Poster: Gradient Estimation Using Stochastic Computation Graphs »
John Schulman · Nicolas Heess · Theophane Weber · Pieter Abbeel -
2015 Poster: Learning Continuous Control Policies by Stochastic Value Gradients »
Nicolas Heess · Gregory Wayne · David Silver · Timothy Lillicrap · Tom Erez · Yuval Tassa -
2014 Poster: Recurrent Models of Visual Attention »
Volodymyr Mnih · Nicolas Heess · Alex Graves · koray kavukcuoglu -
2014 Spotlight: Recurrent Models of Visual Attention »
Volodymyr Mnih · Nicolas Heess · Alex Graves · koray kavukcuoglu