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Workshop

Deep Reinforcement Learning Workshop

Karol Hausman ⋅ Qi Zhang ⋅ Matthew Taylor ⋅ Martha White ⋅ Suraj Nair ⋅ Manan Tomar ⋅ Risto Vuorio ⋅ Ted Xiao ⋅ Zeyu Zheng ⋅ Manan Tomar
Project Page [ Contact: deeprl.workshop.neurips2022@gmail.com ]

Abstract

Video

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Schedule

Timezone: America/Los_Angeles
8:25 AM
Opening Remarks
8:30 AM
Tobias Gerstenberg
Tobias Gerstenberg
9:00 AM
ESCHER: ESCHEWING IMPORTANCE SAMPLING IN GAMES BY COMPUTING A HISTORY VALUE FUNCTION TO ESTIMATE REGRET
Stephen McAleer ⋅ Gabriele Farina ⋅ Marc Lanctot ⋅ Tuomas Sandholm
9:15 AM
Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Jason Yecheng Ma ⋅ Shagun Sodhani ⋅ Dinesh Jayaraman ⋅ Osbert Bastani ⋅ Vikash Kumar ⋅ Amy Zhang
9:45 AM
Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes
Aviral Kumar ⋅ Rishabh Agarwal ⋅ XINYANG GENG ⋅ George Tucker ⋅ Sergey Levine
10:00 AM
Jakob Foerster
Jakob Foerster
11:30 AM
Transformers are Sample-Efficient World Models
Vincent Micheli ⋅ Eloi Alonso ⋅ François Fleuret
11:45 AM
Scaling Laws for a Multi-Agent Reinforcement Learning Model
Oren Neumann ⋅ Claudius Gros
12:00 PM
Natasha Jaques
Natasha Jaques
2:00 PM
Amy Zhang
Amy Zhang
3:00 PM
Igor Mordatch
Igor Mordatch
3:30 PM
John Schulman
John Schulman
3:45 PM
Danijar Hafner
Danijar Hafner
4:00 PM
Kristian Hartikainen
Kristian Hartikainen
4:15 PM
Ilya Kostrikov, Aviral Kumar
Ilya Kostrikov ⋅ Aviral Kumar
5:30 PM
Closing Remarks
Neural All-Pairs Shortest Path for Reinforcement Learning
Cristina Pinneri ⋅ Georg Martius ⋅ Andreas Krause
VI2N: A Network for Planning Under Uncertainty based on Value of Information
Samantha Johnson ⋅ Michael Buice ⋅ Koosha Khalvati
Efficient Multi-Horizon Learning for Off-Policy Reinforcement Learning
Raja Farrukh Ali ⋅ Nasik Muhammad Nafi ⋅ Kevin Duong ⋅ William Hsu
Lagrangian Model Based Reinforcement Learning
Adithya Ramesh ⋅ Balaraman Ravindran
Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines
Andrew Li ⋅ Zizhao Chen ⋅ Pashootan Vaezipoor ⋅ Toryn Klassen ⋅ Rodrigo Toro Icarte ⋅ Sheila McIlraith
Informative rewards and generalization in curriculum learning
Rahul Siripurapu ⋅ Vihang Patil ⋅ Kajetan Schweighofer ⋅ Marius-Constantin Dinu ⋅ Markus Holzleitner ⋅ Hamid Eghbalzadeh ⋅ Luis Ferro ⋅ Thomas Schmied ⋅ Michael Kopp ⋅ Sepp Hochreiter
Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation
Yuzhe Qin ⋅ Binghao Huang ⋅ Zhao-Heng Yin ⋅ Hao Su ⋅ Xiaolong Wang
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
Abdus Salam Azad ⋅ Izzeddin Gur ⋅ Aleksandra Faust ⋅ Pieter Abbeel ⋅ Ion Stoica
The Emphatic Approach to Average-Reward Policy Evaluation
Jiamin He ⋅ Yi Wan ⋅ Rupam Mahmood
Learning Exploration Policies with View-based Intrinsic Rewards
Yijie Guo ⋅ Yao Fu ⋅ Run Peng ⋅ Honglak Lee
Scaling Covariance Matrix Adaptation MAP-Annealing to High-Dimensional Controllers
Bryon Tjanaka ⋅ Matthew Fontaine ⋅ Aniruddha Kalkar ⋅ Stefanos Nikolaidis
Policy Aware Model Learning via Transition Occupancy Matching
Jason Yecheng Ma ⋅ Kausik Sivakumar ⋅ Osbert Bastani ⋅ Dinesh Jayaraman
On The Fragility of Learned Reward Functions
Lev McKinney ⋅ Yawen Duan ⋅ Adam Gleave ⋅ David Krueger
Temporary Goals for Exploration
Haoyang Xu ⋅ Jimmy Ba ⋅ Silviu Pitis ⋅ Harris Chan
Revisiting Bellman Errors for Offline Model Selection
Joshua Zitovsky ⋅ Daniel de Marchi ⋅ Rishabh Agarwal ⋅ Michael Kosorok
Unleashing The Potential of Data Sharing in Ensemble Deep Reinforcement Learning
Zhixuan Lin ⋅ Pierluca D'Oro ⋅ Evgenii Nikishin ⋅ Aaron Courville
What Makes Certain Pre-Trained Visual Representations Better for Robotic Learning?
Kyle Hsu ⋅ Tyler Lum ⋅ Ruohan Gao ⋅ Shixiang (Shane) Gu ⋅ Jiajun Wu ⋅ Chelsea Finn
Curiosity in Hindsight
Daniel Jarrett ⋅ Corentin Tallec ⋅ Florent Altché ⋅ Thomas Mesnard ⋅ Remi Munos ⋅ Michal Valko
Train Offline, Test Online: A Real Robot Learning Benchmark
Gaoyue Zhou ⋅ Victoria Dean ⋅ Mohan Kumar Srirama ⋅ Aravind Rajeswaran ⋅ Jyothish Pari ⋅ Kyle Hatch ⋅ Aryan Jain ⋅ Tianhe Yu ⋅ Pieter Abbeel ⋅ Lerrel Pinto ⋅ Chelsea Finn ⋅ Abhinav Gupta
A Framework for Predictable Actor-Critic Control
Josiah Coad ⋅ James Ault ⋅ Jeff Hykin ⋅ Guni Sharon
Ensemble based uncertainty estimation with overlapping alternative predictions
Dirk Eilers ⋅ Felippe Schmoeller Roza ⋅ Karsten Roscher
Offline Reinforcement Learning on Real Robot with Realistic Data Sources
Gaoyue Zhou ⋅ Liyiming Ke ⋅ Siddhartha Srinivasa ⋅ Abhinav Gupta ⋅ Aravind Rajeswaran ⋅ Vikash Kumar
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments
JB Lanier ⋅ Stephen McAleer ⋅ Pierre Baldi ⋅ Roy Fox
Training Equilibria in Reinforcement Learning
Lauro Langosco ⋅ David Krueger ⋅ Adam Gleave
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Samuel Sokota ⋅ Ryan D'Orazio ⋅ J. Zico Kolter ⋅ Nicolas Loizou ⋅ Marc Lanctot ⋅ Ioannis Mitliagkas ⋅ Noam Brown ⋅ Christian Kroer
Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning
Ali Rahimi-Kalahroudi ⋅ Janarthanan Rajendran ⋅ Ida Momennejad ⋅ Harm Van Seijen ⋅ Sarath Chandar
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
Yanqiu Wu ⋅ Xinyue Chen ⋅ Che Wang ⋅ Yiming Zhang ⋅ Keith Ross
Integrating Episodic and Global Bonuses for Efficient Exploration
Mikael Henaff ⋅ Minqi Jiang ⋅ Roberta Raileanu
Deconfounded Imitation Learning
Risto Vuorio ⋅ Pim de Haan ⋅ Johann Brehmer ⋅ Hanno Ackermann ⋅ Daniel Dijkman ⋅ Taco Cohen
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning
Eddy Hudson ⋅ Ishan Durugkar ⋅ Garrett Warnell ⋅ Peter Stone
Human-AI Coordination via Human-Regularized Search and Learning
Hengyuan Hu ⋅ David Wu ⋅ Adam Lerer ⋅ Jakob Foerster ⋅ Noam Brown
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Jesse Farebrother ⋅ Joshua Greaves ⋅ Rishabh Agarwal ⋅ Charline Le Lan ⋅ Ross Goroshin ⋅ Pablo Samuel Castro ⋅ Marc Bellemare
Return Augmentation gives Supervised RL Temporal Compositionality
Keiran Paster ⋅ Silviu Pitis ⋅ Sheila McIlraith ⋅ Jimmy Ba
Design Process is a Reinforcement Learning Problem
Reza Kakooee ⋅ Benjamin Dillenburger
Bayesian Q-learning With Imperfect Expert Demonstrations
Fengdi Che ⋅ Xiru Zhu ⋅ Doina Precup ⋅ David Meger ⋅ Gregory Dudek
Efficient Deep Reinforcement Learning Requires Regulating Statistical Overfitting
Qiyang Li ⋅ Aviral Kumar ⋅ Ilya Kostrikov ⋅ Sergey Levine
Pre-Training for Robots: Leveraging Diverse Multitask Data via Offline Reinforcement Learning
Anikait Singh ⋅ Aviral Kumar ⋅ Frederik Ebert ⋅ Yanlai Yang ⋅ Chelsea Finn ⋅ Sergey Levine
Offline Reinforcement Learning from Heteroskedastic Data Via Support Constraints
Anikait Singh ⋅ Aviral Kumar ⋅ Quan Vuong ⋅ Yevgen Chebotar ⋅ Sergey Levine
Variance Double-Down: The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning
Johan Obando Ceron ⋅ Marc Bellemare ⋅ Pablo Samuel Castro
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-Oriented Dialogue Systems
Yihao Feng ⋅ Shentao Yang ⋅ Shujian Zhang ⋅ Jianguo Zhang ⋅ Caiming Xiong ⋅ Mingyuan Zhou ⋅ Huan Wang
In the ZONE: Measuring difficulty and progression in curriculum generation
Rose Wang ⋅ Jesse Mu ⋅ Dilip Arumugam ⋅ Natasha Jaques ⋅ Noah Goodman
Better state exploration using action sequence equivalence
Nathan Grinsztajn ⋅ Toby Johnstone ⋅ Johan Ferret ⋅ philippe preux
Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment
Louis Bagot ⋅ Kevin Mets ⋅ Tom De Schepper ⋅ Steven Latre
Guiding Exploration Towards Impactful Actions
Vaibhav Saxena ⋅ Jimmy Ba ⋅ Danijar Hafner
Domain Invariant Q-Learning for model-free robust continuous control under visual distractions
Tom Dupuis ⋅ Jaonary Rabarisoa ⋅ Quoc Cuong PHAM ⋅ David Filliat
Multi-Agent Policy Transfer via Task Relationship Modeling
Rong-Jun Qin ⋅ Feng Chen ⋅ Tonghan Wang ⋅ Lei Yuan ⋅ Xiaoran Wu ⋅ Yipeng Kang ⋅ Zongzhang Zhang ⋅ Chongjie Zhang ⋅ Yang Yu
Foundation Models for History Compression in Reinforcement Learning
Fabian Paischer ⋅ Thomas Adler ⋅ Andreas Radler ⋅ Markus Hofmarcher ⋅ Sepp Hochreiter
A Game-Theoretic Perspective of Generalization in Reinforcement Learning
Chang Yang ⋅ RUIYU WANG ⋅ Xinrun Wang ⋅ Zhen Wang
Imitating Human Behaviour with Diffusion Models
Tim Pearce ⋅ Tabish Rashid ⋅ Anssi Kanervisto ⋅ David Bignell ⋅ Mingfei Sun ⋅ Raluca Georgescu ⋅ Sergio Valcarcel Macua ⋅ Shan Zheng Tan ⋅ Ida Momennejad ⋅ Katja Hofmann ⋅ Sam Devlin
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Yifu Yuan ⋅ Jianye Hao ⋅ Fei Ni ⋅ Yao Mu ⋅ YAN ZHENG ⋅ Yujing Hu ⋅ Jinyi Liu ⋅ Yingfeng Chen ⋅ Changjie Fan
ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation
Pengyi Li ⋅ Hongyao Tang ⋅ Jianye Hao ⋅ YAN ZHENG ⋅ Xian Fu ⋅ Zhaopeng Meng
Quantization-aware Policy Distillation (QPD)
Thomas Avé ⋅ Kevin Mets ⋅ Tom De Schepper ⋅ Steven Latre
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
Michał Zawalski ⋅ Michał Tyrolski ⋅ Konrad Czechowski ⋅ Damian Stachura ⋅ Piotr Piękos ⋅ Tomasz Odrzygóźdź ⋅ Yuhuai Wu ⋅ Łukasz Kuciński ⋅ Piotr Miłoś
Cyclophobic Reinforcement Learning
Stefan Wagner ⋅ Peter Arndt ⋅ Jan Robine ⋅ Stefan Harmeling
AsymQ: Asymmetric Q-loss to mitigate overestimation bias in off-policy reinforcement learning
Qinsheng Zhang ⋅ Arjun Krishna ⋅ Sehoon Ha ⋅ Yongxin Chen
Fine-tuning Offline Policies with Optimistic Action Selection
Max Sobol Mark ⋅ Ali Ghadirzadeh ⋅ Xi Chen ⋅ Chelsea Finn
SEM2: Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model
Zeyu Gao ⋅ Yao Mu ⋅ Ruoyan Shen ⋅ Chen Chen ⋅ Yangang Ren ⋅ Jianyu Chen ⋅ Shengbo Li ⋅ Ping Luo ⋅ Yanfeng Lu
Policy Architectures for Compositional Generalization in Control
Allan Zhou ⋅ Vikash Kumar ⋅ Chelsea Finn ⋅ Aravind Rajeswaran
Rethinking Learning Dynamics in RL using Adversarial Networks
Ramnath Kumar ⋅ Tristan Deleu ⋅ Yoshua Bengio
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
Jiachen Li ⋅ Shuo Cheng ⋅ Zhenyu Liao ⋅ Huayan Wang ⋅ William Yang Wang ⋅ Qinxun Bai
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
Stone Tao ⋅ Xiaochen Li ⋅ Tongzhou Mu ⋅ Zhiao Huang ⋅ Yuzhe Qin ⋅ Hao Su
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier
Pierluca D'Oro ⋅ Max Schwarzer ⋅ Evgenii Nikishin ⋅ Pierre-Luc Bacon ⋅ Marc Bellemare ⋅ Aaron Courville
Adversarial Policies Beat Professional-Level Go AIs
Tony Wang ⋅ Adam Gleave ⋅ Nora Belrose ⋅ Tom Tseng ⋅ Michael Dennis ⋅ Yawen Duan ⋅ Viktor Pogrebniak ⋅ Joseph Miller ⋅ Sergey Levine ⋅ Stuart J Russell
VARIATIONAL REPARAMETRIZED POLICY LEARNING WITH DIFFERENTIABLE PHYSICS
Zhiao Huang ⋅ Litian Liang ⋅ Zhan Ling ⋅ Xuanlin Li ⋅ Chuang Gan ⋅ Hao Su
Efficient Multi-Task Reinforcement Learning via Selective Behavior Sharing
Grace Zhang ⋅ Ayush Jain ⋅ Injune Hwang ⋅ Shao-Hua Sun ⋅ Joseph Lim
Contrastive Example-Based Control
Kyle Hatch ⋅ Sarthak J Shetty ⋅ Benjamin Eysenbach ⋅ Tianhe Yu ⋅ Rafael Rafailov ⋅ Russ Salakhutdinov ⋅ Sergey Levine ⋅ Chelsea Finn
Multi-skill Mobile Manipulation for Object Rearrangement
Jiayuan Gu ⋅ Devendra Singh Chaplot ⋅ Hao Su ⋅ Jitendra Malik
Visual Reinforcement Learning with Self-Supervised 3D Representations
Yanjie Ze ⋅ Nicklas Hansen ⋅ Yinbo Chen ⋅ Mohit Jain ⋅ Xiaolong Wang
Building a Subspace of Policies for Scalable Continual Learning
Jean-Baptiste Gaya ⋅ Thang Long Doan ⋅ Lucas Page-Caccia ⋅ Laure Soulier ⋅ Ludovic Denoyer ⋅ Roberta Raileanu
Skill Machines: Temporal Logic Composition in Reinforcement Learning
Geraud Nangue Tasse ⋅ Devon Jarvis ⋅ Steven James ⋅ Benjamin Rosman
Learning Representations for Reinforcement Learning with Hierarchical Forward Models
Trevor McInroe ⋅ Lukas Schäfer ⋅ Stefano Albrecht
In-context Reinforcement Learning with Algorithm Distillation
Michael Laskin ⋅ Luyu Wang ⋅ Junhyuk Oh ⋅ Emilio Parisotto ⋅ Stephen Spencer ⋅ Richie Steigerwald ⋅ DJ Strouse ⋅ Steven Hansen ⋅ Angelos Filos ⋅ Ethan Brooks ⋅ Maxime Gazeau ⋅ Himanshu Sahni ⋅ Satinder Singh ⋅ Volodymyr Mnih
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
Nicklas Hansen ⋅ Yixin Lin ⋅ Hao Su ⋅ Xiaolong Wang ⋅ Vikash Kumar ⋅ Aravind Rajeswaran
Graph Inverse Reinforcement Learning from Diverse Videos
Sateesh Kumar ⋅ Jonathan Zamora ⋅ Nicklas Hansen ⋅ Rishabh Jangir ⋅ Xiaolong Wang
Adversarial Cheap Talk
Chris Lu ⋅ Timon Willi ⋅ Alistair Letcher ⋅ Jakob Foerster
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
yifan xu ⋅ Nicklas Hansen ⋅ Zirui Wang ⋅ Yung-Chieh Chan ⋅ Hao Su ⋅ Zhuowen Tu
SPRINT: Scalable Semantic Policy Pre-training via Language Instruction Relabeling
Jesse Zhang ⋅ Karl Pertsch ⋅ Jiahui Zhang ⋅ Taewook Nam ⋅ Sung Ju Hwang ⋅ Xiang Ren ⋅ Joseph Lim
Towards True Lossless Sparse Communication in Multi-Agent Systems
Seth Karten ⋅ Mycal Tucker ⋅ Siva Kailas ⋅ Katia Sycara
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
Anton Bakhtin ⋅ David Wu ⋅ Adam Lerer ⋅ Jonathan Gray ⋅ Athul Jacob ⋅ Gabriele Farina ⋅ Alexander Miller ⋅ Noam Brown
PnP-Nav: Plug-and-Play Policies for Generalizable Visual Navigation Across Robots
Dhruv Shah ⋅ Ajay Sridhar ⋅ Arjun Bhorkar ⋅ Noriaki Hirose ⋅ Sergey Levine
Offline Reinforcement Learning for Customizable Visual Navigation
Dhruv Shah ⋅ Arjun Bhorkar ⋅ Hrishit Leen ⋅ Ilya Kostrikov ⋅ Nicholas Rhinehart ⋅ Sergey Levine
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning
Remo Sasso ⋅ Matthia Sabatelli ⋅ Marco Wiering
Hyperbolic Deep Reinforcement Learning
Edoardo Cetin ⋅ Benjamin Chamberlain ⋅ Michael Bronstein ⋅ jonathan j hunt
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning
Adrien Ali Taiga ⋅ Rishabh Agarwal ⋅ Jesse Farebrother ⋅ Aaron Courville ⋅ Marc Bellemare
Efficient Exploration using Model-Based Quality-Diversity with Gradients
Bryan Lim ⋅ Manon Flageat ⋅ Antoine Cully
Choreographer: Learning and Adapting Skills in Imagination
Pietro Mazzaglia ⋅ Tim Verbelen ⋅ Bart Dhoedt ⋅ Alexandre Lacoste ⋅ Sai Rajeswar Mudumba
Efficient Offline Policy Optimization with a Learned Model
Zichen Liu ⋅ Siyi Li ⋅ Wee Sun Lee ⋅ Shuicheng Yan ⋅ Zhongwen Xu
Emergent collective intelligence from massive-agent cooperation and competition
Hanmo Chen ⋅ Stone Tao ⋅ JIAXIN CHEN ⋅ Weihan Shen ⋅ Xihui Li ⋅ Chenghui Yu ⋅ Sikai Cheng ⋅ Xiaolong Zhu ⋅ Xiu Li
Distance-Sensitive Offline Reinforcement Learning
Li Jianxiong ⋅ Xianyuan Zhan ⋅ Haoran Xu ⋅ Xiangyu Zhu ⋅ Jingjing Liu ⋅ Ya-Qin Zhang
Uncertainty-Driven Exploration for Generalization in Reinforcement Learning
Yiding Jiang ⋅ J. Zico Kolter ⋅ Roberta Raileanu
Language Models Can Teach Themselves to Program Better
Patrick Haluptzok ⋅ Matthew Bowers ⋅ Adam Kalai
Graph Q-Learning for Combinatorial Optimization
Victoria Magdalena Dax ⋅ Jiachen Li ⋅ Kevin Leahy ⋅ Mykel J Kochenderfer
Transformer-based World Models Are Happy With 100k Interactions
Jan Robine ⋅ Marc Höftmann ⋅ Tobias Uelwer ⋅ Stefan Harmeling
Contrastive Value Learning: Implicit Models for Simple Offline RL
Bogdan Mazoure ⋅ Benjamin Eysenbach ⋅ Ofir Nachum ⋅ Jonathan Tompson
CASA: Bridging the Gap between Policy Improvement and Policy Evaluation with Conflict Averse Policy Iteration
Changnan Xiao ⋅ Haosen Shi ⋅ Jiajun Fan ⋅ Shihong Deng ⋅ Haiyan Yin
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
Mikayel Samvelyan ⋅ Akbir Khan ⋅ Michael Dennis ⋅ Minqi Jiang ⋅ Jack Parker-Holder ⋅ Jakob Foerster ⋅ Roberta Raileanu ⋅ Tim Rocktäschel
Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning
Onno Eberhard ⋅ Jakob Hollenstein ⋅ Cristina Pinneri ⋅ Georg Martius
Evaluating Long-Term Memory in 3D Mazes
Jurgis Pašukonis ⋅ Timothy Lillicrap ⋅ Danijar Hafner
Visual Imitation Learning with Patch Rewards
Minghuan Liu ⋅ Tairan He ⋅ Weinan Zhang ⋅ Shuicheng Yan ⋅ Zhongwen Xu
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning
Mhairi Dunion ⋅ Trevor McInroe ⋅ Kevin Sebastian Luck ⋅ Josiah Hanna ⋅ Stefano Albrecht
Momentum Boosted Episodic Memory for Improving Learning in Long-Tailed RL Environments
Dolton Fernandes ⋅ Pramod Kaushik ⋅ Harsh Shukla ⋅ Raju Bapi
A Ranking Game for Imitation Learning
Harshit Sushil Sikchi ⋅ Akanksha Saran ⋅ Wonjoon Goo ⋅ Scott Niekum
Implicit Offline Reinforcement Learning via Supervised Learning
Alexandre Piche ⋅ Rafael Pardinas ⋅ David Vazquez ⋅ Igor Mordatch ⋅ Igor Mordatch ⋅ Chris Pal
Distributional deep Q-learning with CVaR regression
Mastane Achab ⋅ REDA ALAMI ⋅ YASSER ABDELAZIZ DAHOU DJILALI ⋅ Kirill Fedyanin ⋅ Eric Moulines ⋅ Maxim Panov
The Surprising Effectiveness of Latent World Models for Continual Reinforcement Learning
Samuel Kessler ⋅ Piotr Miłoś ⋅ Jack Parker-Holder ⋅ S Roberts
Perturbed Quantile Regression for Distributional Reinforcement Learning
Taehyun Cho ⋅ Seungyub Han ⋅ Heesoo Lee ⋅ Kyungjae Lee ⋅ Jungwoo Lee
Concept-based Understanding of Emergent Multi-Agent Behavior
Niko Grupen ⋅ Shayegan Omidshafiei ⋅ Natasha Jaques ⋅ Been Kim
Constrained Imitation Q-learning with Earth Mover’s Distance reward
WENYAN Yang ⋅ Nataliya Strokina ⋅ Joni Pajarinen ⋅ Joni-kristian Kamarainen
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement
Michael Chang ⋅ Alyssa L Dayan ⋅ Franziska Meier ⋅ Tom Griffiths ⋅ Sergey Levine ⋅ Amy Zhang
SoftTreeMax: Policy Gradient with Tree Search
Gal Dalal ⋅ Assaf Hallak ⋅ Shie Mannor ⋅ Gal Chechik
Hypernetwork-PPO for Continual Reinforcement Learning
Philemon Schöpf ⋅ Sayantan Auddy ⋅ Jakob Hollenstein ⋅ Antonio Rodriguez-sanchez
Actor Prioritized Experience Replay
Baturay Saglam ⋅ Furkan Burak Mutlu ⋅ Doğan Can Çiçek ⋅ Suleyman Kozat
Model and Method: Training-Time Attack for Cooperative Multi-Agent Reinforcement Learning
Siyang Wu ⋅ Tonghan Wang ⋅ Xiaoran Wu ⋅ Jingfeng ZHANG ⋅ Yujing Hu ⋅ Changjie Fan ⋅ Chongjie Zhang
Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning
Chaitanya Kharyal ⋅ Tanmay Sinha ⋅ Vijaya Sai Krishna Gottipati ⋅ Srijita Das ⋅ Matthew Taylor
On All-Action Policy Gradients
Michal Nauman ⋅ Marek Cygan
A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning
Benjamin Eysenbach ⋅ Matthieu Geist ⋅ Russ Salakhutdinov ⋅ Sergey Levine
The Benefits of Model-Based Generalization in Reinforcement Learning
Kenny Young ⋅ Aditya Ramesh ⋅ Louis Kirsch ⋅ Jürgen Schmidhuber
Training graph neural networks with policy gradients to perform tree search
Matthew Macfarlane ⋅ Diederik Roijers ⋅ Herke van Hoof
Co-Imitation: Learning Design and Behaviour by Imitation
Chang Rajani ⋅ Karol Arndt ⋅ David Blanco-Mulero ⋅ Kevin Sebastian Luck ⋅ Ville Kyrki
BLaDE: Robust Exploration via Diffusion Models
Bilal Piot ⋅ Zhaohan Guo ⋅ Shantanu Thakoor ⋅ Mohammad Gheshlaghi Azar
Learning Semantics-Aware Locomotion Skills from Human Demonstrations
Yuxiang Yang ⋅ Xiangyun Meng ⋅ Wenhao Yu ⋅ Tingnan Zhang ⋅ Jie Tan ⋅ Byron Boots
Imitation from Observation With Bootstrapped Contrastive Learning
Medric Sonwa ⋅ Johanna Hansen ⋅ Eugene Belilovsky
PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm
Toygun Basaklar ⋅ Suat Gumussoy ⋅ Umit Ogras
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
Matthias Gerstgrasser ⋅ Tom Danino ⋅ Sarah Keren
Planning Immediate Landmarks of Targets for Model-Free Skill Transfer across Agents
Minghuan Liu ⋅ Zhengbang Zhu ⋅ Menghui Zhu ⋅ Yuzheng Zhuang ⋅ Weinan Zhang ⋅ Jianye Hao
Sample-efficient Adversarial Imitation Learning
Dahuin Jung ⋅ Hyungyu Lee ⋅ Sungroh Yoon
Prioritizing Samples in Reinforcement Learning with Reducible Loss
Shivakanth Sujit ⋅ Somjit Nath ⋅ Pedro Braga ⋅ Samira Ebrahimi Kahou
PCRL: Priority Convention Reinforcement Learning for Microscopically Sequencable Multi-agent Problems
Xing Zhou ⋅ Hao Gao ⋅ Xin Xu ⋅ Xinglong Zhang ⋅ Hongda Jia ⋅ Dongzi Wang
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
Zixiang Chen ⋅ Chris Junchi Li ⋅ Angela Yuan ⋅ Quanquan Gu ⋅ Michael Jordan
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective
Raj Ghugare ⋅ Homanga Bharadhwaj ⋅ Benjamin Eysenbach ⋅ Sergey Levine ⋅ Ruslan Salakhutdinov
Reinforcement Learning in System Identification
Jose Martin H. ⋅ Óscar Fernandez Vicente ⋅ Sergio Perez ⋅ Anas Belfadil ⋅ Cristina Ibanez-Llano ⋅ Freddy Perozo Rondón ⋅ Jose Valle ⋅ Javier Arechalde Pelaz
Robust Option Learning for Adversarial Generalization
Kishor Jothimurugan ⋅ Steve Hsu ⋅ Osbert Bastani ⋅ Rajeev Alur
Biological Neurons vs Deep Reinforcement Learning: Sample efficiency in a simulated game-world
Forough Habibollahi ⋅ Moein Khajehnejad ⋅ Amitesh Gaurav ⋅ Brett J. Kagan
Learning Successor Feature Representations to Train Robust Policies for Multi-task Learning
Melissa Mozifian ⋅ Dieter Fox ⋅ David Meger ⋅ Fabio Ramos ⋅ Animesh Garg
Supervised Q-Learning for Continuous Control
Hao Sun ⋅ Ziping Xu ⋅ Taiyi Wang ⋅ Meng Fang ⋅ Bolei Zhou
MOPA: a Minimalist Off-Policy Approach to Safe-RL
Hao Sun ⋅ Ziping Xu ⋅ Zhenghao Peng ⋅ Meng Fang ⋅ Bo Dai ⋅ Bolei Zhou
Novel Policy Seeking with Constrained Optimization
Hao Sun ⋅ Zhenghao Peng ⋅ Bolei Zhou