About

In recent years, the use of deep neural networks as function approximators has enabled researchers to extend reinforcement learning techniques to solve increasingly complex control tasks. The emerging field of deep reinforcement learning has led to remarkable empirical results in rich and varied domains like robotics, strategy games, and multi-agent interactions. This workshop will bring together researchers working at the intersection of deep learning and reinforcement learning, and it will help interested researchers outside of the field gain a high-level view about the current state of the art and potential directions for future contributions.

For previous editions, please visit NeurIPS 2020, 2019, 2018, 2017, 2016, 2015.

Attending the Workshop

The workshop will be live-streamed at this link: https://neurips.cc/virtual/2021/workshop/21848. Chats to ask questions, GatherTown links for poster sessions, and all pre-recorded videos can be viewed on this site. NeurIPS workshop registration is required.

Important Dates and Deadlines

• Paper submission deadline: September 17th October 8th 2021

• Submission site: https://openreview.net/group?id=NeurIPS.cc/2021/Workshop/DeepRL

• Call for Papers & formatting instruction: [link]

• ***New this year*** Call for Opinion Talk Abstracts: [link]

• Author Notification: October 22nd 2021

• Q&A: deeprl.workshop.neurips2021@gmail.com

• Workshop date: December 13th 2021

• Workshop Time: 8:15am - 7:00pm PST

Invited Speakers

Schedule (December 13th 2021, 8:55am-6:00pm PST)

Gather Town Links: Room 1 Room 2 Room 3 Room 4

• 08:55 - 09:00 Welcome and Introduction

• 09:00 - 10:00 Contributed talks

  • 09:00 - 09:15 Implicit Behavioral Cloning

  • 09:15 - 09:30 DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization

  • 09:30 - 09:45 HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation

  • 09:45 - 10:00 Benchmarking the Spectrum of Agent Capabilities

• 10:00 - 10:30 Invited Talk: Laura Schulz - In praise of folly: Goals, play, and human cognition

• 10:30 - 11:00 BREAK

• 11:00 - 11:30 Opinion Contributed Talk: Wilka Carvalho

• 11:30 - 12:00 Contributed talks

  • 11:30 - 11:45 Adaptive Scheduling of Data Augmentation for Deep Reinforcement Learning

  • 11:45 - 12:00 Offline Meta-Reinforcement Learning with Online Self-Supervision

• 12:00 - 12:30 Invited Talk: George Konidaris - Signal to Symbol (via Skills)

• 12:30 - 13:30 Poster session [Lunch]

• 13:30 - 14:00 Opinion Contributed Talk: Sergey Levine

• 14:00 - 14:30 Panel discussion 1 [authors of all contributed and opinion talks]

• 14:30 - 15:00 Invited Talk: Dale Schuurmans - Understanding Deep Value Estimation

• 15:00 - 15:30 BREAK

• 15:30 - 16:00 Invited Talk: Karol Hausman - Reinforcement Learning as a Data Sponge

• 16:00 - 16:30 NeurIPS RL Competitions Results Presentations

• 16:30 - 17:00 Invited Talk: Kenji Doya - Natural and Artificial Reinforcement Learning

• 17:00 - 18:00 Panel discussion 2 [speakers from all invited talks]

Organizers

Advisory Board

Contributed Talks

• HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation

Boyan Li, Hongyao Tang, Yan Zheng, Jianye Hao, Pengyi Li, Zhen Wang, Zhaopeng Meng, Li Wang

• DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization

Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker, Sergey Levine

• Implicit Behavioral Cloning

Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, Jonathan Tompson

• Offline Meta-Reinforcement Learning with Online Self-Supervision

Vitchyr H. Pong, Ashvin Nair, Laura Smith, Catherine Huang, Sergey Levine

• Adaptive Scheduling of Data Augmentation for Deep Reinforcement Learning

Byungchan Ko, Jungseul Ok

• Benchmarking the Spectrum of Agent Capabilities

Danijar Hafner

Accepted Posters

• ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

Toshinori Kitamura, Ryo Yonetani

• Graph Backup: Data Efficient Backup Exploiting Markovian Data

Zhengyao Jiang, Tianjun Zhang, Robert Kirk, Tim Rocktäschel, Edward Grefenstette

• From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation

Yuzhe Qin, Hao Su, Xiaolong Wang

• TransDreamer: Reinforcement Learning with Transformer World Models

Chang Chen, Jaesik Yoon, Yi-Fu Wu, Sungjin Ahn

• Task-Induced Representation Learning

Jun Yama, Karl Pertsch, Anisha Gunjal, Joseph J Lim

• The Reflective Explorer: Online Meta-Exploration from Offline Data in Realistic Robotic Tasks

Rafael Rafailov, Varun Kumar Vijay, Tianhe Yu, Avi Singh, Mariano Phielipp, Chelsea Finn

• Mismatched No More: Joint Model-Policy Optimization for Model-Based RL

Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov

• Generalisation in Lifelong Reinforcement Learning through Logical Composition

Geraud Nangue Tasse, Steven James, Benjamin Rosman

• Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games

Dingyang Chen, Yile Li, Qi Zhang

• Meta Arcade: A Configurable Environment Suite for Deep Reinforcement Learning and Meta-Learning

Edward W Staley, Chace Ashcraft, Benjamin Stoler, Jared Markowitz, Gautam Vallabha, Christopher Ratto, Kapil Katyal

• Maximum Entropy Model-based Reinforcement Learning

Oleg Svidchenko, Aleksei Shpilman

• MHER: Model-based Hindsight Experience Replay

Rui Yang, Meng Fang, Lei Han, Yali Du, Feng Luo, Xiu Li

• Stability Analysis in Mixed-Autonomous Traffic with Deep Reinforcement Learning

Dongsu Lee, Minhae Kwon

• Grounding Aleatoric Uncertainty in Unsupervised Environment Design

Minqi Jiang, Michael D Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Kuttler, Edward Grefenstette, Tim Rocktäschel, Jakob Nicolaus Foerster

• Robust Robotic Control from Pixels using Contrastive Recurrent State-Space Models

Nitish Srivastava, Walter Talbott, Martin Bertran Lopez, Shuangfei Zhai, Joshua M. Susskind

• Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari

Dominik Schmidt, Thomas Schmied

• That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities

Jack Parker-Holder, Minqi Jiang, Michael D Dennis, Mikayel Samvelyan, Jakob Nicolaus Foerster, Edward Grefenstette, Tim Rocktäschel

• Behavior Predictive Representations for Generalization in Reinforcement Learning

Siddhant Agarwal, Aaron Courville, Rishabh Agarwal

• OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning

Jinyi Liu, Zhi Wang, YAN ZHENG, Jianye HAO, Junjie Ye, Chenjia Bai, Pengyi Li

• Dynamic Mirror Descent based Model Predictive Control for Accelerating Robot Learning

Utkarsh Aashu Mishra, Soumya Rani Samineni, Prakhar Goel, Chandravaran Venkatasai Kunjeti, Himanshu Lodha, Aman Singh, Aditya Verma Sagi, Shalabh Bhatnagar, Shishir N Y

• Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation

Rishabh Jangir, Nicklas Hansen, Sambaran Ghosal, Mohit Jain, Xiaolong Wang

• PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy Network

Pei Xu, Ioannis Karamouzas

• Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Yecheng Jason Ma, Andrew Shen, Osbert Bastani, Dinesh Jayaraman

• Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning

Kajetan Schweighofer, Markus Hofmarcher, Marius-Constantin Dinu, Philipp Renz, Angela Bitto-Nemling, Vihang Prakash Patil, Sepp Hochreiter

• Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto

• Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization

Masahiro Kato, Kei Nakagawa, Kenshi Abe, Tetsuro Morimura

• CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery

Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel

• URLB: Unsupervised Reinforcement Learning Benchmark

Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel

• Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning

Nicolai Dorka, Joschka Boedecker, Wolfram Burgard

• Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization

Zihan Zhou, Wei Fu, Bingliang Zhang, Yi Wu

• Hybrid Imitative Planning with Geometric and Predictive Costs in Offroad Environments

Nitish Dashora, Daniel Shin, Dhruv Shah, Henry Leopold, David Fan, Ali Agha, Nicholas Rhinehart, Sergey Levine

• Strength Through Diversity: Robust Behavior Learning via Mixture Policies

Tim Seyde, Wilko Schwarting|Igor Gilitschenski, Markus Wulfmeier, Daniela Rus

• Generalized Decision Transformer for Offline Hindsight Information Matching

Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu

• Accelerated Deep Reinforcement Learning of Terrain-Adaptive Locomotion Skills

Khaled S. Refaat, Kai Ding

• Policy Optimization via Optimal Policy Evaluation

Alberto Maria Metelli, Samuele Meta, Marcello Restelli

• Status-quo policy gradient in Multi-Agent Reinforcement Learning

Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri, Jayakumar Subramanian, Abhishek Sinha, Siddharth Singh, Balaji Krishnamurthy

• Component Transfer Learning for Deep RL Based on Abstract Representations

Geoffrey Van Driess, Vincent Francois-Lavet

• Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments

Ken Ming Lee, Sriram Ganapathi Subramanian, Mark Crowley

• Large Scale Coordination Transfer for Cooperative Multi-Agent Reinforcement Learning

Ethan Wang, Binghong Chen, Le Song

• Interactive Robust Policy Optimization for Multi-Agent Reinforcement Learning

Videh Raj Nema, Balaraman Ravindran

• The Information Geometry of Unsupervised Reinforcement Learning

Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

• What Would the Expert $do(\cdot)$?: Causal Imitation Learning

Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu

• Learning Efficient Multi-Agent Cooperative Visual Exploration

Chao Yu, Xinyi Yang, Jiaxuan Gao, Huazhong Yang, Yu Wang, Yi Wu

• Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation

Todor Davchev, Oleg Sushkov, Jean-Baptiste Regli, Stefan Schaal, Yusuf Aytar, Markus Wulfmeier, Jon Scholz

• A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems

Xian Yeow Lee, Soumik Sarkar, Yubo Wang

• Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning

Dhruv Shah, Peng Xu, Yao Lu, Ted Xiao, Alexander T Toshev, Sergey Levine, Brian Ichter

• General Characterization of Agents by States they Visit

Anssi Kanervisto, Tomi Kinnunen, Ville Hautamaki

• Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay

Iryna Korshunova, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel, Edward Grefenstette

• Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL

Yanchao Sun, Ruijie Zheng, Yongyuan Liang, Furong Huang

• Automatic Curricula via Expert Demonstrations

Siyu Dai, Andreas Hofmann, Brian C. Williams

• Recurrent Off-policy Baselines for Memory-based Continuous Control

Zhihan Yang, Hai Huu Nguyen

• Self-Imitation Learning from Demonstrations

George Pshihachev, Dmitry Ivanov, Vladimir Egorov, Aleksei Shpilman

• Understanding and Preventing Capacity Loss in Reinforcement Learning

Clare Lyle, Mark Rowland, Will Dabney

• Variance-Seeking Meta-Exploration to Handle Out-of-Distribution Tasks

Yashvir Singh Grewal, Frits de Nijs, Sarah Goodwin

• A Closer Look at Gradient Estimators with Reinforcement Learning as Inference

Jonathan Wilder Lavington, Michael Teng, Mark Schmidt, Frank Wood

• Attention-based Partial Decoupling of Policy and Value for Generalization in Reinforcement Learning

Nasik Muhammad Nafi, Creighton Glasscock, William Hsu

• Imitation Learning from Observations under Transition Model Disparity

Tanmay Gangwani, Yuan Zhou, Jian Peng

• Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization

Chieko Sarah Imai, Minghao Zhang, Yuchen Zhang, Marcin Kierebiński, Ruihan Yang, Yuzhe Qin, Xiaolong Wang

• Learning from demonstrations with SACR2: Soft Actor-Critic with Reward Relabeling

Jesús Bujalance Martín, Raphaël Chekroun, Fabien Moutarde

• Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification

Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu

• DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

Fei Deng, Ingook Jang, Sungjin Ahn

• Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates

Litian Liang, Yaosheng Xu, Stephen Marcus McAleer, Dailin Hu, Alexander Ihler, Pieter Abbeel, Roy Fox

• Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method

Duo Xu

• Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers

Ruihan Yang, Minghao Zhang, Nicklas Hansen, Huazhe Xu, Xiaolong Wang

• Learning Value Functions from Undirected State-only Experience

Matthew Chang, Arjun Gupta, Saurabh Gupta

• Target Entropy Annealing for Discrete Soft Actor-Critic

Yaosheng Xu, Dailin Hu, Litian Liang, Stephen Marcus McAleer, Pieter Abbeel, Roy Fox

• Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks

Yijie Guo, Qiucheng Wu, Honglak Lee

• Follow the Object: Curriculum Learning for Manipulation Tasks with Imagined Goals

Ozsel Kilinc, Giovanni Montana

• BLAST: Latent Dynamics Models from Bootstrapping

Keiran Paster, Lev E McKinney, Sheila A. McIlraith, Jimmy Ba

• Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning

Dailin Hu, Pieter Abbeel, Roy Fox

• Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning

Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Chelsea Finn, Sergey Levine, Karol Hausman

• StarCraft II Unplugged: Large Scale Offline Reinforcement Learning

Michael Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Caglar Gulcehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Konrad Zolna, Richard Powell, Julian Schrittwieser, David Choi, Petko Georgiev, Daniel Kenji Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aaron van den Oord, Wojciech M. Czarnecki, Nando de Freitas, Oriol Vinyals

• Learning Robust Dynamics through Variational Sparse Gating

Arnav Kumar Jain, Shiva Kanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou

• Should I Run Offline Reinforcement Learning or Behavioral Cloning?

Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine

• Deep RePReL--Combining Planning and Deep RL for acting in relational domains

Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli

• Fast Inference and Transfer of Compositional Task for Few-shot Task Generalization

Sungryull Sohn, Hyunjae Woo, Jongwook Choi, lyubing qiang, Izzeddin Gur, Aleksandra Faust, Honglak Lee

• Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning

Aaqib Parvez Mohammed, Matias Valdenegro-Toro

• Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning

Trevor Ablett, Bryan Chan, Jonathan Kelly

• Off-Policy Correction For Multi-Agent Reinforcement Learning

Michał Zawalski, Błażej Osiński, Henryk Michalewski, Piotr Miłoś

• Bayesian Exploration for Lifelong Reinforcement Learning

Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris

• A Modern Self-Referential Weight Matrix That Learns to Modify Itself

Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber

• Offline Policy Selection under Uncertainty

Mengjiao Yang, Bo Dai, Ofir Nachum, George Tucker, Dale Schuurmans

• 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

• Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks

Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus

• Cross-Domain Imitation Learning via Optimal Transport

Arnaud Fickinger, Samuel Cohen, Stuart Russell, Brandon Amos

• Lifting the veil on hyper-parameters for value-based deep reinforcement learning

João Guilherme Madeira Araújo, Johan Samir Obando Ceron, Pablo Samuel Castro

• Reward Uncertainty for Exploration in Preference-based Reinforcement Learning

Xinran Liang, Katherine Shu, Kimin Lee, Pieter Abbeel

• Learning Parameterized Task Structure for Generalization to Unseen Entities

Anthony Zhe Liu, Sungryull Sohn, Mahdi Qazwini, Honglak Lee

• The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models

Alexander Pan, Kush Bhatia, Jacob Steinhardt

• Learning a Subspace of Policies for Online Adaptation in Reinforcement Learning

Jean-Baptiste Gaya, Laure Soulier, Ludovic Denoyer

• Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations

Haoran Xu, Xianyuan Zhan, Honglei Yin, Huiling Qin

• Task-driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning

Wilka Torrico Carvalho, Andrew Kyle Lampinen, Kyriacos Nikiforou, Felix Hill, Murray Shanahan

• Hindsight Foresight Relabeling for Meta-Reinforcement Learning

Michael Wan, Jian Peng, Tanmay Gangwani

• Continuous Control With Ensemble Deep Deterministic Policy Gradients

Piotr Januszewski, Mateusz Olko, Michał Królikowski, Jakub Swiatkowski, Marcin Andrychowicz, Łukasz Kuciński, Piotr Miłoś

• SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning

Jongjin Park, Younggyo Seo, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee

• GrASP: Gradient-Based Affordance Selection for Planning

Vivek Veeriah, Zeyu Zheng, Richard Lewis, Satinder Singh

• Beyond Target Networks: Improving Deep $Q$-learning with Functional Regularization

Alexandre Piché, Joseph Marino, Gian Maria Marconi, Valentin Thomas, Christopher Pal, Mohammad Emtiyaz Khan

• No DICE: An Investigation of the Bias-Variance Tradeoff in Meta-Gradients

Risto Vuorio, Jacob Austin Beck, Gregory Farquhar, Jakob Nicolaus Foerster, Shimon Whiteson

• Block Contextual MDPs for Continual Learning

Shagun Sodhani, Franziska Meier, Joelle Pineau, Amy Zhang

• A Framework for Efficient Robotic Manipulation

Albert Zhan, Ruihan Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin

• Transfer RL across Observation Feature Spaces via Model-Based Regularization

Yanchao Sun, Ruijie Zheng, Xiyao Wang, Andrew E Cohen, Furong Huang

• Embodiment perspective of reward definition for behavioural homeostasis

Naoto Yoshida, Tatsuya Daikoku, Yukie Nagai, Yasuo Kuniyoshi

• Offline Reinforcement Learning with Implicit Q-Learning

Ilya Kostrikov, Ashvin Nair, Sergey Levine

• Wasserstein Distance Maximizing Intrinsic Control

Ishan Durugkar, Steven Stenberg Hansen, Stephen Spencer, Volodymyr Mnih

• Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks

Soroush Nasiriany, Huihan Liu, Yuke Zhu

• Long-Term Credit Assignment via Model-based Temporal Shortcuts

Michel Ma, Pierluca D'Oro, Yoshua Bengio, Pierre-Luc Bacon

• C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks

Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez

• TARGETED ENVIRONMENT DESIGN FROM OFFLINE DATA

Izzeddin Gur, Ofir Nachum, Aleksandra Faust

• GPU-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning

Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, Michael Jordan

• Policy Gradients Incorporating the Future

David Venuto, Elaine Lau, Doina Precup, Ofir Nachum

• TempoRL: Temporal Priors for Exploration in Off-Policy Reinforcement Learning

Marco Bagatella, Sammy Joe Christen, Otmar Hilliges

• Exploring through Random Curiosity with General Value Functions

Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

• Exponential Family Model-Based Reinforcement Learning via Score Matching

Gene Li, Junbo Li, Nathan Srebro, Zhaoran Wang, Zhuoran Yang

• Imitation Learning from Pixel Observations for Continuous Control

Samuel Cohen, Brandon Amos, Marc Peter Deisenroth, Mikael Henaff, Eugene Vinitsky, Denis Yarats

• Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning

Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit

• Latent Geodesics of Model Dynamics for Offline Reinforcement Learning

Guy Tennenholtz, Nir Baram, Shie Mannor

• An Empirical Study of Non-Uniform Sampling in Off-Policy Reinforcement Learning for Continuous Control

Nicholas Ioannidis, Jonathan Wilder Lavington, Mark Schmidt

• On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension

Udari Madhushani, Biswadip Dey, Naomi Leonard, Amit Chakraborty

• Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback

Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi, Pieter Abbeel, Michael Laskin

• Unsupervised Learning of Temporal Abstractions using Slot-based Transformers

Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste

• Modern Hopfield Networks for Return Decomposition for Delayed Rewards

Michael Widrich, Markus Hofmarcher, Vihang Prakash Patil, Angela Bitto-Nemling, Sepp Hochreiter

• Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium

Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael Jordan

• Learning compositional tasks from language instructions

Lajanugen Logeswaran, Wilka Torrico Carvalho, Honglak Lee

• Deep Reinforcement Learning Explanation via Model Transforms

Mira Finkelstein, Sarah Keren, Nitsan Levy Schlot, Lucy Liu, Yoav Kolumbus, Jeffrey Rosenschein, David C. Parkes

• A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning

Adrian Brasoveanu, Rohan Pandey, Maximilian Emerson Alfano-Smith

• OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion

Vittorio La Barbera, Fabio Pardo, Yuval Tassa, Monica Daley, Christopher Richards, Petar Kormushev, John Hutchinson

• CoMPS: Continual Meta Policy Search

Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, Sergey Levine

• Continuous Control with Action Quantization from Demonstrations

Robert Dadashi, Leonard Hussenot, Damien Vincent, Sertan Girgin, Anton Raichuk, Matthieu Geist, Olivier Pietquin

• Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation

Ryuji Imamura, Takuma Seno, Kenta Kawamoto, Michael Spranger

• On the Transferability of Deep-Q Networks

Matthia Sabatelli, Pierre Geurts

• Skill-based Meta-Reinforcement Learning

Taewook Nam, Shao-Hua Sun, Karl Pertsch, Sung Ju Hwang, Joseph J Lim

• Introducing Symmetries to Black Box Meta Reinforcement Learning

Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram L. Friesen, Junhyuk Oh, Yutian Chen

• Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL

Catherine Cang, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin

• Math Programming based Reinforcement Learning for Multi-Echelon Inventory Management

Pavithra Harsha, Ashish Jagmohan, Jayant Kalagnanam, Brian Quanz, Divya Singhvi

• Implicitly Regularized RL with Implicit Q-values

Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist

• Towards Automatic Actor-Critic Solutions to Continuous Control

Jake Grigsby, Jin Yong Yoo, Yanjun Qi

• Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World Trifinger

Arthur Allshire, Mayank Mittal, Varun Lodaya, Viktor Makoviychuk, Denys Makoviichuk, Felix Widmaier, Manuel Wuthrich, Stefan Bauer, Ankur Handa, Animesh Garg

• Hierarchical Few-Shot Imitation with Skill Transition Models

Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel, Michael Laskin

• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives

Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov

• A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning

Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio

• Discriminator Augmented Model-Based Reinforcement Learning

Behzad Haghgoo, Allan Zhou, Archit Sharma, Chelsea Finn