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Workshop

Deep Reinforcement Learning

Pieter Abbeel ⋅ Chelsea Finn ⋅ David Silver ⋅ Matthew Taylor ⋅ Martha White ⋅ Srijita Das ⋅ Yuqing Du ⋅ Andrew Patterson ⋅ Manan Tomar ⋅ Olivia Watkins
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Abstract

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Schedule

Timezone: America/Los_Angeles
9:00 AM
Implicit Behavioral Cloning
Pete Florence ⋅ Corey Lynch ⋅ Andy Zeng ⋅ Oscar Ramirez ⋅ Ayzaan Wahid ⋅ Laura Downs ⋅ Adrian Wong ⋅ Igor Mordatch ⋅ Jonathan Tompson
9:12 AM
Implicit Behavioral Cloning Q&A
Pete Florence ⋅ Corey Lynch ⋅ Andy Zeng ⋅ Oscar Ramirez ⋅ Ayzaan Wahid ⋅ Laura Downs ⋅ Adrian Wong ⋅ Igor Mordatch ⋅ Jonathan Tompson
9:15 AM
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Aviral Kumar ⋅ Rishabh Agarwal ⋅ Tengyu Ma ⋅ Aaron Courville ⋅ George Tucker ⋅ Sergey Levine
9:27 AM
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization Q&A
Aviral Kumar ⋅ Rishabh Agarwal ⋅ Tengyu Ma ⋅ Aaron Courville ⋅ George Tucker ⋅ Sergey Levine
9:30 AM
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Boyan Li ⋅ Hongyao Tang ⋅ YAN ZHENG ⋅ Jianye Hao ⋅ Pengyi Li ⋅ Zhaopeng Meng ⋅ LI Wang
9:42 AM
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation Q&A
Boyan Li ⋅ Hongyao Tang ⋅ YAN ZHENG ⋅ Jianye Hao ⋅ Pengyi Li ⋅ Zhaopeng Meng ⋅ LI Wang
10:25 AM
Laura Schulz Talk Q&A
Laura Schulz
10:30 AM
Break
11:00 AM
Opinion Contributed Talk: Wilka Carvalho
Wilka Carvalho Carvalho
11:25 AM
Wilka Carvalho Talk Q&A
Wilka Carvalho Carvalho
11:45 AM
Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr Pong ⋅ Ashvin Nair ⋅ Laura Smith ⋅ Catherine Huang ⋅ Sergey Levine
11:57 AM
Offline Meta-Reinforcement Learning with Online Self-Supervision Q&A
Vitchyr Pong ⋅ Ashvin Nair ⋅ Laura Smith ⋅ Catherine Huang ⋅ Sergey Levine
12:25 PM
George Konidaris Talk Q&A
George Konidaris
1:55 PM
Sergey Levine Talk Q&A
Sergey Levine
2:55 PM
Dale Schuurmans Talk Q&A
Dale Schuurmans
3:00 PM
Break
3:55 PM
Karol Hausman Talk Q&A
Karol Hausman
4:00 PM
NeurIPS RL Competitions Results Presentations
Rohin Shah ⋅ Liam Paull ⋅ Tabitha Lee ⋅ Tim Rocktäschel ⋅ Heinrich Küttler ⋅ Sharada Mohanty ⋅ Manuel Wuethrich
4:55 PM
Kenji Doya Talk Q&A
Kenji Doya
Self-Imitation Learning from Demonstrations
Georgiy Pshikhachev ⋅ Dmitry Ivanov ⋅ Vladimir Egorov ⋅ Aleksei Shpilman
Understanding and Preventing Capacity Loss in Reinforcement Learning
Clare Lyle ⋅ Mark Rowland ⋅ Will Dabney
A Closer Look at Gradient Estimators with Reinforcement Learning as Inference
Jonathan Lavington ⋅ Michael Teng ⋅ Mark Schmidt ⋅ Frank Wood
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
Minghao Zhang ⋅ Ruihan Yang ⋅ Yuzhe Qin ⋅ Xiaolong Wang
Learning from demonstrations with SACR2: Soft Actor-Critic with Reward Relabeling
Jesús Bujalance Martín ⋅ Raphael Chekroun ⋅ Fabien Moutarde
Generalisation in Lifelong Reinforcement Learning through Logical Composition
Geraud Nangue Tasse ⋅ Steven James ⋅ Benjamin Rosman
Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates
Litian Liang ⋅ Yaosheng Xu ⋅ Stephen McAleer ⋅ Dailin Hu ⋅ Alexander Ihler ⋅ Pieter Abbeel ⋅ Roy Fox
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 McAleer ⋅ Pieter Abbeel ⋅ Roy Fox
The Reflective Explorer: Online Meta-Exploration from Offline Data in Realistic Robotic Tasks
Rafael Rafailov ⋅ ⋅ Tianhe Yu ⋅ Avi Singh ⋅ Mariano Phielipp ⋅ Chelsea Finn
BLAST: Latent Dynamics Models from Bootstrapping
Keiran Paster ⋅ Lev McKinney ⋅ Sheila McIlraith ⋅ Jimmy Ba
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
Dhruv Shah ⋅ Ted Xiao ⋅ Alexander Toshev ⋅ Sergey Levine ⋅ brian ichter
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 Paine ⋅ Konrad Żołna ⋅ Julian Schrittwieser ⋅ David Choi ⋅ Petko I Georgiev ⋅ Daniel Toyama ⋅ Roman Ring ⋅ Igor Babuschkin ⋅ Timo Ewalds ⋅ ⋅ Aaron van den Oord ⋅ Wojciech Czarnecki ⋅ Nando de Freitas ⋅ Oriol Vinyals
Learning Robust Dynamics through Variational Sparse Gating
Arnav Kumar Jain ⋅ Shivakanth 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
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Aviral Kumar ⋅ Rishabh Agarwal ⋅ Tengyu Ma ⋅ Aaron Courville ⋅ George Tucker ⋅ 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 ⋅ Izzeddin Gur ⋅ Aleksandra Faust ⋅ Honglak Lee
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning
Aaqib Parvez Mohammed ⋅ Matias Valdenegro-Toro
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
Distributional Decision Transformer for Offline Hindsight Information Matching
Hiroki Furuta ⋅ Yutaka Matsuo ⋅ Shixiang (Shane) Gu
Offline Policy Selection under Uncertainty
Mengjiao (Sherry) 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
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 ⋅ Jonathan Scholz
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-baseddeep reinforcement learning
João Madeira Araújo ⋅ Johan Obando Ceron ⋅ Pablo Samuel Castro
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Xinran Liang ⋅ Katherine Shu ⋅ Kimin Lee ⋅ Pieter Abbeel
TransDreamer: Reinforcement Learning with Transformer World Models
⋅ Jaesik Yoon ⋅ Yi-Fu Wu ⋅ Sungjin Ahn
Learning a Subspace of Policies for Online Adaptation in Reinforcement Learning
Jean-Baptiste Gaya ⋅ Laure Soulier ⋅ Ludovic Denoyer
Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
Nicolai Dorka ⋅ Joschka Boedecker ⋅ Wolfram Burgard
Task-driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning
Wilka Carvalho ⋅ Andrew Lampinen ⋅ Kyriacos Nikiforou ⋅ Felix Hill ⋅ Murray Shanahan
Hindsight Foresight Relabeling for Meta-Reinforcement Learning
Michael Wan ⋅ Jian Peng ⋅ Tanmay Gangwani
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
Misha Laskin ⋅ Hao Liu ⋅ Xue Bin Peng ⋅ Denis Yarats ⋅ Aravind Rajeswaran ⋅ Pieter Abbeel
Continuous Control With Ensemble Deep Deterministic Policy Gradients
Piotr Januszewski ⋅ Mateusz Olko ⋅ Michał Królikowski ⋅ Jakub Swiatkowski ⋅ Marcin Andrychowicz ⋅ Łukasz Kuciński ⋅ Piotr Miłoś
What Would the Expert $do(\cdot)$?: Causal Imitation Learning
Gokul Swamy ⋅ Sanjiban Choudhury ⋅ James Bagnell ⋅ Steven Wu
Grounding Aleatoric Uncertainty in Unsupervised Environment Design
Minqi Jiang ⋅ Michael Dennis ⋅ Jack Parker-Holder ⋅ Andrei Lupu ⋅ Heinrich Kuttler ⋅ Edward Grefenstette ⋅ Tim Rocktäschel ⋅ Jakob Foerster
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
Task-Induced Representation Learning
Jun Yamada ⋅ Karl Pertsch ⋅ Anisha Gunjal ⋅ Joseph Lim
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
GrASP: Gradient-Based Affordance Selection for Planning
Vivek Veeriah ⋅ Zeyu Zheng ⋅ Richard L Lewis ⋅ Satinder Singh
Beyond Target Networks: Improving Deep $Q$-learning with Functional Regularization
Alexandre Piche ⋅ Joseph Marino ⋅ Gian Maria Marconi ⋅ Valentin Thomas ⋅ Chris Pal ⋅ Mohammad Emtiyaz Khan
No DICE: An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
Risto Vuorio ⋅ Jacob Beck ⋅ Greg Farquhar ⋅ Jakob 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 ⋅ Misha Laskin
Transfer RL across Observation Feature Spaces via Model-Based Regularization
Yanchao Sun ⋅ Ruijie Zheng ⋅ Xiyao Wang ⋅ Andrew Cohen ⋅ Furong Huang
URLB: Unsupervised Reinforcement Learning Benchmark
Misha Laskin ⋅ Denis Yarats ⋅ Hao Liu ⋅ Kimin Lee ⋅ Albert Zhan ⋅ Kevin Lu ⋅ Catherine Cang ⋅ Lerrel Pinto ⋅ Pieter Abbeel
Offline Reinforcement Learning with In-sample Q-Learning
Ilya Kostrikov ⋅ Ashvin Nair ⋅ Sergey Levine
Wasserstein Distance Maximizing Intrinsic Control
Ishan Durugkar ⋅ Steven Hansen ⋅ Stephen Spencer ⋅ Volodymyr Mnih ⋅ Ishan Durugkar
Strength Through Diversity: Robust Behavior Learning via Mixture Policies
Tim Seyde ⋅ Wilko Schwarting ⋅ Igor Gilitschenski ⋅ Markus Wulfmeier ⋅ Daniela Rus
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 ⋅ Ben Eysenbach ⋅ Russ Salakhutdinov ⋅ Sergey Levine ⋅ Joseph Gonzalez
General Characterization of Agents by States they Visit
Anssi Kanervisto ⋅ Ville Hautamäki
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 ⋅ Zhuoran Yang ⋅ Zhaoran Wang ⋅ Anwar Walid ⋅ Jian Guo ⋅ Michael Jordan
Behavior Predictive Representations for Generalization in Reinforcement Learning
Siddhant Agarwal ⋅ Aaron Courville ⋅ Rishabh Agarwal
Implicit Behavioral Cloning
Pete Florence ⋅ Corey Lynch ⋅ Andy Zeng ⋅ Oscar Ramirez ⋅ Ayzaan Wahid ⋅ Laura Downs ⋅ Adrian Wong ⋅ Igor Mordatch ⋅ Jonathan Tompson
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 Christen ⋅ Otmar Hilliges
Dynamic Mirror Descent based Model Predictive Control for Accelerating Robot Learning
Utkarsh A Mishra ⋅ Soumya Samineni ⋅ Shalabh Bhatnagar ⋅ Shishir N Y
Exploring through Random Curiosity with General Value Functions
Aditya Ramesh ⋅ Louis Kirsch ⋅ Sjoerd van Steenkiste ⋅ Jürgen Schmidhuber
Maximum Entropy Model-based Reinforcement Learning
Oleg Svidchenko ⋅ Aleksei Shpilman
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 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
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 ⋅ Misha Laskin
That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities
Jack Parker-Holder ⋅ Minqi Jiang ⋅ Michael Dennis ⋅ Mikayel Samvelyan ⋅ Jakob Foerster ⋅ Edward Grefenstette ⋅ Tim Rocktäschel
The Information Geometry of Unsupervised Reinforcement Learning
Ben Eysenbach ⋅ Russ Salakhutdinov ⋅ Sergey Levine
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
Ben Eysenbach ⋅ Alexander Khazatsky ⋅ Sergey Levine ⋅ Russ Salakhutdinov
Graph Backup: Data Efficient Backup Exploiting Markovian Data
zhengyao Jiang ⋅ Tianjun Zhang ⋅ Robert Kirk ⋅ Tim Rocktäschel ⋅ Edward Grefenstette
Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr Pong ⋅ Ashvin Nair ⋅ Laura Smith ⋅ Catherine Huang ⋅ Sergey Levine
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 Patil ⋅ Angela Bitto ⋅ Sepp Hochreiter
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium
Chris Junchi Li ⋅ Dongruo Zhou ⋅ Quanquan Gu ⋅ Michael Jordan
Understanding the Effects of Dataset Composition on Offline Reinforcement Learning
Kajetan Schweighofer ⋅ Markus Hofmarcher ⋅ Marius-Constantin Dinu ⋅ Philipp Renz ⋅ Angela Bitto ⋅ Vihang Patil ⋅ Sepp Hochreiter
Learning Efficient Multi-Agent Cooperative Visual Exploration
Chao Yu ⋅ Jiaxuan Gao ⋅ Huazhong Yang ⋅ Yu Wang ⋅ Yi Wu
Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization
Masahiro Kato ⋅ Kei Nakagawa ⋅ Kenshi Abe ⋅ Tetsuro Morimura
Learning compositional tasks from language instructions
Lajanugen Logeswaran ⋅ Wilka Carvalho ⋅ Honglak Lee
Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay
Iryna Korshunova ⋅ Minqi Jiang ⋅ Jack Parker-Holder ⋅ Tim Rocktäschel ⋅ Edward Grefenstette
Status-quo policy gradient in Multi-Agent Reinforcement Learning
Pinkesh Badjatiya ⋅ Mausoom Sarkar ⋅ Nikaash Puri ⋅ Jayakumar Subramanian ⋅ Abhishek Sinha ⋅ Siddharth Singh ⋅ Balaji Krishnamurthy
Deep Reinforcement Learning Explanation via Model Transforms
Sarah Keren ⋅ Yoav Kolumbus ⋅ Jeffrey S Rosenschein ⋅ David Parkes ⋅ Mira Finkelstein
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning
Adrian Brasoveanu ⋅ Rohan Pandey ⋅ Maximilian Alfano-Smith
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion
Vittorio La Barbera ⋅ Fabio Pardo ⋅ Yuval Tassa ⋅ Petar Kormushev ⋅ John Hutchinson
Hybrid Imitative Planning with Geometric and Predictive Costs in Offroad Environments
Daniel Shin ⋅ Dhruv Shah ⋅ Ali Agha ⋅ Nicholas Rhinehart ⋅ Sergey Levine
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 ⋅ Anton Raichuk ⋅ Matthieu Geist ⋅ Olivier Pietquin
MHER: Model-based Hindsight Experience Replay
Yang Rui ⋅ Meng Fang ⋅ Lei Han ⋅ Yali Du ⋅ Feng Luo ⋅ Xiu Li
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 Lim
Introducing Symmetries to Black Box Meta Reinforcement Learning
Louis Kirsch ⋅ Sebastian Flennerhag ⋅ Hado van Hasselt ⋅ Abram Friesen ⋅ Junhyuk Oh ⋅ Yutian Chen
Robust Robotic Control from Pixels using Contrastive Recurrent State-Space Models
Nitish Srivastava ⋅ Walter Talbott ⋅ Shuangfei Zhai ⋅ Joshua Susskind
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Boyan Li ⋅ Hongyao Tang ⋅ YAN ZHENG ⋅ Jianye Hao ⋅ Pengyi Li ⋅ Zhaopeng Meng ⋅ LI Wang
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL
Catherine Cang ⋅ Aravind Rajeswaran ⋅ Pieter Abbeel ⋅ Misha Laskin
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning
Jason Yecheng Ma ⋅ Andrew Shen ⋅ Osbert Bastani ⋅ Dinesh Jayaraman
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 Wuethrich ⋅ Stefan Bauer ⋅ Ankur Handa ⋅ Animesh Garg
Hierarchical Few-Shot Imitation with Skill Transition Models
Kourosh Hakhamaneshi ⋅ Ruihan Zhao ⋅ Albert Zhan ⋅ Pieter Abbeel ⋅ Misha Laskin
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives
Murtaza Dalal ⋅ Deepak Pathak ⋅ Russ Salakhutdinov
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 Williams
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
Denis Yarats ⋅ Rob Fergus ⋅ Alessandro Lazaric ⋅ Lerrel Pinto
Policy Optimization via Optimal Policy Evaluation
Alberto Maria Metelli ⋅ Samuele Meta ⋅ Marcello Restelli
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
Allan Zhou ⋅ Archit Sharma ⋅ Chelsea Finn