RLC 2026 Paper Schedule
Natural Policy Gradient for Bayesian Network Policies in Markov Potential Games
Dingyang Chen, Zhenyu Zhang, Yuan Ling, and Qi Zhang
StaQ: a Finite Memory Approach to Discrete Action Policy Mirror Descent
Alex Davey, Alena Shilova, Brahim Driss, and Riad Akrour
Towards Formalizing Reinforcement Learning Theory: A Robbins-Siegmund Approach
Shangtong Zhang
Intrinsic Closed-Loop Practical Asymptotic Stability in Discrete-Time Reinforcement Learning
Jan de Priester and Ricardo Sanfelice
Provable Distributional Value Iteration under Partial Observability
Larry Preuett, Qiuyi Zhang, and Muhammad Aurangzeb Ahmad
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification
Haoyang Hong, Zichen Wang, Quanquan Gu, and Huazheng Wang
Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation
Jake Gonzales, Max Horwitz, Eric Mazumdar, and Lillian J. Ratliff
The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
Sign-SZPO: Provable Preference-based Reinforcement Learning with an Unknown Link Function
Qining Zhang and Lei Ying
Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See
Mohamed Benabdelouahad, AHMED DJALAL HACINI, Nadir Farhi, and Aissa Boulmerka
Reward Design Agent for Reinforcement Learning
Hojoon Lee, Ajay Subramanian, Ben Abbatematteo, Vijay Veerabadran, Pedro Matias, Karl Ridgeway, and Nitin Kamra
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
Ali Larian, Qian Lin, Chang Zong Wu, and Daniel S. Brown
Escaping Offline Pessimism: Vector-Field Reward Shaping for Safe Frontier Exploration
Amirhossein Roknilamouki, Arnob Ghosh, Eylem Ekici, and Ness Shroff
Leveraging Reward Machines for Efficient Multi-Objective Reinforcement Learning
Panos Aronis, Mehdi Dastani, Roxana Rădulescu, and Giovanni Varricchione
SR-Reward: Taking The Path More Traveled
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning
Hsiao-Ru Pan and Bernhard Schölkopf
Gradient Iterated Temporal-Difference Learning
Théo Vincent, Kevin Gerhardt, Yogesh Tripathi, Habib Maraqten, Adam White, Martha White, Jan Peters, and Carlo D'Eramo
Representation Regularization in Distributional Reinforcement Learning
André Inge, Jonas Nordqvist, Björn Lindenberg, and Karl-Olof Lindahl
From Pixels to Factors: Learning Independently Controllable State Variables for Reinforcement Learning
Rafael Rodriguez-Sanchez, Cameron Allen, and George Konidaris
Learning World Value Functions with Successor Representation and Vision Models
Sergio Frasco, Devon Jarvis, and Geraud Nangue Tasse
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
Mingxuan Che, Tsung Yuan Tseng, Theresa Eimer, Marius Lindauer, and Alexander von Rohr
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling
The Yokai Learning Environment: Tracking Beliefs Over Space and Time
Constantin Ruhdorfer, Matteo Bortoletto, Johannes Forkel, Jakob Nicolaus Foerster, and Andreas Bulling
The Cell Must Go On: Agar.io for Continual Reinforcement Learning
Mohamed Ayman Mohamed, Kateryna Nekhomiazh, Vedant Vyas, Marcos Menon Jose, Andrew Patterson, and Marlos C. Machado
SegDAC: Visual Generalization in Reinforcement Learning via Dynamic Object Tokens
Alexandre Brown and Glen Berseth
Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Program
Jose Efraim Aguilar Escamilla, Lingdong Zhou, Xiangqi Zhu, and Huazheng Wang
Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius, Elle Miller, Trevor McInroe, Fan Zhang, Patricia Wollstadt, Stefano V. Albrecht, and Subramanian Ramamoorthy
The Open Ant: A Robot Platform for Reinforcement Learning Research
Elena Sorina Lupu, Patrick Spieler, Khurram Javed, Kris De Asis, John D Martin, Martha Steenstrup, and Joseph Varughese Modayil
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning
Vincent Taboga, Justin Veilleux, Doseok Jang, Anushree Rankawat, and Pierre-Luc Bacon
Randomized Exploration for Linear Bandits via Absolute Perturbations
Toshinori Kitamura, Shuai Liu, and Csaba Szepesvari
ContrastSpanner: Learning Low-Rank Causal Contrasts to Alleviate Power-Set and Eluder Barriers
Alec Koppel and Laixi Shi
Best-of-Both-Worlds Multi-Dueling Bandits: Unified Algorithms for Stochastic and Adversarial Preferences under Condorcet and Borda Objectives
S Akash, Pratik Gajane, and Jawar Singh
Offline-to-Online Learning in Linear Bandits
Kushagra Chandak, Toshinori Kitamura, and Xiaoqi Tan
Annealed Softmax Greedy in Many-Armed Bayesian Bandits
William Overman and Mohsen Bayati
Collaborative Learning under Strategic Behavior: Mechanisms for Eliciting Feedback in Principal-Agent Bandit Games
Ramakrishnan Krishnamurthy, Arpit Agarwal, Lakshmi Subramanian, and Maximilian Nickel
Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
Udvas Das, Waris Radji, Debabrota Basu, and Odalric-Ambrym Maillard
Limits of reinforcement learning for decision trees in Markov decision processes
Hector Kohler, Riad Akrour, and Philippe Preux
Planning for Signaling in Low-Trust Environments
Septia Rani, Turgay Caglar, and Sarath Sreedharan
Inference-Time Policy Alignment for Fair Reinforcement Learning
Umer Siddique, Peilang Li, Conor Wallace, and Yongcan Cao
Improving Human Performance with Value-Aware Interventions: A Case Study in Chess
Saumik Narayanan, Raja Panjwani, Siddhartha Sen, and Chien-Ju Ho
Toward Agents That Reason About Their Computation
Adrian Orenstein, Jessica Chen, Gwyneth Anne Delos Santos, Bayley Sapara, and Michael Bowling
Let it Cook: Learning to Wait in Sequential Decision Making
Christopher Watson, Arjun Krishna, Dinesh Jayaraman, and Rajeev Alur
Trajectory First: A Curriculum for Discovering Diverse Policies
Cornelius V. Braun, Sayantan Auddy, and Marc Toussaint
Hierarchical Behaviour Spaces
Michael Matthews, Pierluca D'Oro, Anssi Kanervisto, Scott Fujimoto, Jakob Nicolaus Foerster, and Mikael Henaff
Overcoming Valid Action Suppression in Unmasked Policy Gradient Algorithms
Renos Zabounidis, Roy Siegelmann, Mohamad Qadri, Woojun Kim, Simon Stepputtis, and Katia P. Sycara
PPO+: Enhancing proximal policy optimization
Mahdi Kallel, Jose-Luis Holgado-Alvarez, Samuele Tosatto, and Carlo D'Eramo
Repetition as Reinforcement: Enhancing Sample Efficiency via Instant Episode Repetition in Reinforcement Learning
Hoda Yamani, Yuning Xing, Koen van Rijnsoever, Bruce A. MacDonald, and Henry Williams
Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments
Michael Beukman, Khimya Khetarpal, Zeyu Zheng, Will Dabney, Jakob Nicolaus Foerster, Michael D Dennis, and Clare Lyle
Counterfactual Shapley Credit Assignment
Mingxuan Li, Kai-Zhan Lee, and Elias Bareinboim
Goal-Oriented Reinforcement Learning for Stochastic Shortest Paths with Dead-Ends
Gustavo De Mari Pereira and Leliane N. de Barros
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
Dillon Sandhu and Ronald Parr
Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry
Alexander Fabisch, Melvin Laux, Mariela De Lucas Alvarez, Edoardo Caroselli, and Julian Theis
Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids
Hadi Nekoei, Alexandre Blondin Massé, Rachid Hassani, Sarath Chandar, and Vincent Mai
Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning
Jiajun Hu, Núria Armengol Urpí, Jin Cheng, and Stelian Coros
Dynamics Models for Offline Hyperparameter Selection in Real-World RL
Jordan Coblin, Han Wang, Martha White, and Adam White
Dynamic Object Masks as Goal Representations for Visual Goal-Conditioned Reinforcement Learning
Fahim Shahriar, Cheryl Wang, Seyed Alireza Azimi, Gautham Vasan, Hany Hamed, Abhishek Naik, A. Rupam Mahmood, and Colin Bellinger
Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach
Alessandro Sestini, Joakim Bergdahl, Jean-Philippe Barrette-LaPierre, Florian Fuchs, Brady Chen, Fabio Zinno, Michael D Jones, and Linus Gisslén
When Do We Need LLMs? A Diagnostic for Language-Driven Bandits
Uljad Berdica, Fernando Acero, Anton Ipsen, Parisa Zehtabi, Michael Cashmore, and Manuela Veloso
Discovering High Quality Chess Puzzles with Offline Reinforcement Learning
Allen Nie, Anirudhan Badrinath, Nicholas Tomlin, Timothy Dai, Carissa Yip, Rose E Wang, Emma Brunskill, and Christopher J Piech
Learning When to Stop: Prefix-Optimal Dynamic Diffusion Policies for Continuous Control
Rohit Kumar Salla, Manoj Saravanan, and Simon Stepputtis
Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning
Aleksandar Todorov and Matthia Sabatelli
Momba: Network Modernization Improves Multi-Objective Reinforcement Learning
Adam Štafa, Santeri Heiskanen, Petr Novotný, and Joni Pajarinen
Revisiting FTA: A Sparse One-to-Many Activation for Reinforcement Learning
Tyler Lazar, Matthew Vandergrift, Martha White, and Adam White
Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
Taewoon Kim, Vincent Francois-Lavet, and Michael Cochez
Learning the Supports for Categorical Critic in Reinforcement Learning
Jen-Yen Chang, Takayuki Osa, and Tatsuya Harada
Rethinking the Suitability of RL Algorithms Under Practical Transfer Constraints
Hany Hamed, Abhishek Naik, Colin Bellinger, and A. Rupam Mahmood
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control
Donghu Kim, Youngdo Lee, Hojoon Lee, Johan Obando-Ceron, Byungkun Lee, Aaron Courville, Pablo Samuel Castro, Jaegul Choo, and Clare Lyle
From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
Christian Gumbsch, Leonardo Barcellona, Lennard Schuenemann, Platon Karageorgis, Andrii Zadaianchuk, Zehao Wang, Sergey Zakharov, Fabien Despinoy, Rahaf Aljundi, and Stratis Gavves
ICPL: Few-shot In-context Preference Learning via LLMs
Chao Yu, Qixin Tan, Hong Lu, Jiaxuan Gao, Xinting Yang, Yu Wang, Yi Wu, and Eugene Vinitsky
Modification-Considering Value Learning for Reward Hacking Mitigation in RL
Evgenii Opryshko, Umangi Jain, and Igor Gilitschenski
PB²: Preference Space Exploration via Population-Based Methods in Preference-Based Reinforcement Learning
Brahim Driss, Alex Davey, and Riad Akrour
Discovering Reinforcement Learning Interfaces with Large Language Models
Akshat Singh Jaswal, Ashish Baghel, and Paras Chopra
Generalization in Monitored Markov Decision Processes (Mon-MDPs)
Montaser Mohammedalamen and Michael Bowling
Design Principles for Tabular Multi-Policy MORL in Infinite Horizons
Marcelo d'Almeida and Daniel Mosse
Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization
Matteo Pannacci, Andrea Fanti, Elena Umili, and Roberto Capobianco
Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Dynamics Models
Julia Berger, Bernd Frauenknecht, Sebastian Trimpe, and Bastian Leibe
Risk-Aware General-Utility Markov Decision Processes
Pedro Pinto Santos, Fábio Vital, Alberto Sardinha, and Francisco S. Melo
Stable Planning through Aligned Representations in Model-Based Reinforcement Learning
Misagh Soltani and Forest Agostinelli
Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality
Akhil S Anand, Shambhuraj Sawant, Paavo Parmas, Jasper Hoffmann, Dirk Reinhardt, and Sebastien Gros
Using Common Random Numbers for Simulation-based Planning with Rollouts
Sandarbh Yadav, Frederic J Maliakkal, Harshad Khadilkar, and Shivaram Kalyanakrishnan
Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions
Junlin Xiao, Victor-Alexandru Darvariu, Bruno Lacerda, and Nick Hawes
Strategically-Linked Decisions in Long-Term Planning and Reinforcement Learning
Alihan Hüyük, Jonas B Raedler, Leo Benac, and Finale Doshi-Velez
When to Plan: Learning to Select Between Reactive Control and Deliberative Planning
Adam Labiosa and Josiah P. Hanna
Centralized Adaptive Sampling for Reliable Co-training of Independent Multi-Agent Policies
Nicholas E. Corrado and Josiah P. Hanna
Beyond Local Views: Global State Inference with Diffusion Models for Cooperative MARL
Zhiwei Xu, Hangyu Mao, ZHANG NIANMIN, Shengtao Zhang, Xin Xin, Pengjie Ren, Dapeng Li, Bin Zhang, Guoliang Fan, Zhumin Chen, Changwei Wang, and Jiangjin Yin
Credit Assignment and Focused Exploration for Sparse-reward Multi-agent Deep Reinforcement Learning
Shuai Han, Mehdi Dastani, and Shihan Wang
Multi-Agent Reinforcement Learning with Reward Machines for Mixed Cooperative-Competitive Environments
Sriram Ganapathi Subramanian, Toryn Q. Klassen, and Sheila A. McIlraith
Learning Multi-Agent Communication Protocol: Study on Information Entropy Efficiency in MARL
Xinren Zhang, Zixin Zhong, and Jiadong Yu
Coordination Graphs for Constrained Multi-Agent Reinforcement Learning
Santiago Amaya-Corredor, Miguel Calvo-Fullana, and Anders Jonsson
The challenge of hidden gifts in multi-agent reinforcement learning
Dane Malenfant and Blake Aaron Richards
Sociodynamics of Reinforcement Learning
On the Variance of Temporal Difference Learning and its Reduction Using Control Variates
Hsiao-Ru Pan and Bernhard Schölkopf
Near-Optimal Reinforcement Learning for Linear Distributionally Robust Markov Decision Processes
Zhishuai Liu, Weixin Wang, and Pan Xu
Statistical Inference for Policy Evaluation with Temporal Difference Learning
Weichen Wu, Gen Li, Yuting Wei, and Alessandro Rinaldo
Finite Time Analysis of the Natural Policy Gradient in Finite-Horizon Markov Decision Processes
Asha Barua and Sajad Khodadadian
Solvable models of learning to pursue a moving target
John J. Vastola and Kanaka Rajan
Rationalizing Boltzmann Rationality: An Axiomatic Characterization of Entropy-Regularized Policies
Silviu Pitis
Optimal Regret for Policy Optimization in Average Reward MDPs Without Mixing
William Powell, Jeongyeol Kwon, Qiaomin Xie, and Hanbaek Lyu
When Can Pure Exploitation Succeed in Linear RL? Decoys and Self-Identifiability for Greedy LSVI
Manoj Saravanan and Rohit Kumar Salla
On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents
Oliver Mortensen and M. Sadegh Talebi
Conformal Preemption of Failures in Sequential Decision-Making Agents
Garrett Ethan Katz, Adebayo Braimah, Qinru Qiu, and Simon Khan
Distributionally Robust Self Paced Curriculum Reinforcement Learning
Anirudh Satheesh, Keenan Powell, and Vaneet Aggarwal
Adaptive Critic Shaping for Reinforcement Learning with Temporal Logic Constraint
Duo XU
Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank, Henrik Hose, and Sebastian Trimpe
An Unreasonably Simple Approach to Safe RL
Geraud Nangue Tasse, Mark Nemecek, Tamlin Love, Steven James, and Benjamin Rosman
Skill-based Safe Reinforcement Learning with Risk Planning
Hanping Zhang and Yuhong Guo
Yes, Q-learning Helps Offline In-Context RL
Denis Tarasov, Alexander Nikulin, Ilya Zisman, Albina Klepach, Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin, Igor Kiselev, and Vladislav Kurenkov
CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning
Marcel Hedman, Kale-ab Tessera, Juan Claude Formanek, Anya Sims, Riccardo Zamboni, Trevor McInroe, John Torr, and Elliot Fosong
Fully Offline Reinforcement Learning
Mattie Fellows, Clarisse Wibault, Uljad Berdica, Johannes Forkel, Michael A Osborne, and Jakob Nicolaus Foerster
V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions
Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
Mumuksh Tayal, Manan Tayal, and Ravi Prakash
Conservative Value Priors: A Bayesian Path to Offline Reinforcement Learning
Filippo Valdettaro, Yingzhen Li, and Aldo A. Faisal
{MOORL}: A Framework for Integrating Offline-Online Reinforcement Learning
Confidence Intervals for the Interquartile Mean
Alexandra Burushkina and Philip S. Thomas
Synthetic Monitoring Environments for Reinforcement Learning
Leonard S. Pleiss, Carolin Schmidt, and Maximilian Schiffer
Memory Retention Is Not Enough to Master Memory Tasks in Reinforcement Learning
Oleg Shchendrigin, Egor Cherepanov, Alexey Kovalev, and Aleksandr Panov
PGTG: Procedurally Generated Grid-Based Traffic Gym
Joshua Meyer, Felix Maurice Kuntz, Verena Wolf, Jörg Hoffmann, and Timo P. Gros
Ludax: A GPU-Accelerated Description Language for Board Games
Graham Todd, Alexander George Padula, Dennis J. N. J. Soemers, Sam Earle, and Julian Togelius
Prediction-Based Markov Violation Scores for Detecting Non-Markovian Observations in Reinforcement Learning
Naveen Mysore
NutriRL: A Benchmark for Nutritional Regulation under Delayed State Transitions
Aniket Khan, Charitha Palika, and V.Srinivasa Chakravarthy
Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots
Khurram Javed, Joseph Varughese Modayil, Gloria Kennickell, Richard S Sutton, and John Carmack
DART: Dual Adaptive Residual Tracking for Low-Bias Advantage Estimation and Credit Assignment in AI Agents
Shahrad Mohammadzadeh, Amir-massoud Farahmand, Reihaneh Rabbany, and Doina Precup
FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies
Chenxiao Gao, Edward Chen, Tianyi Chen, and Bo Dai
A Simple Baseline for Learning Approximate State Abstractions in Factored State Spaces
Anshuman Senapati and Josiah P. Hanna
Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning
Parham Mohammad Panahi, Armin Ashrafi, Haoyu Du, Andrew Patterson, Martha White, and Adam White
Improving Reward-Based Hindsight Credit Assignment
Aditya A. Ramesh, Jiamin He, Jürgen Schmidhuber, and Martha White
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
Andrea Fraschini, Davide Tenedini, Riccardo Zamboni, Mirco Mutti, and Marcello Restelli
A Survey of State Representation Learning for Deep Reinforcement Learning
Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, and Roberto Martín-Martín
A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation
Xiaocan Li, Shiliang Wu, and Zheng Shen
Scalable Causal Imitation Learning
Eylam Tagor, Mingxuan Li, and Elias Bareinboim
Supervised Reward Inference
Will Schwarzer, Jordan Jack Schneider, Philip S. Thomas, and Scott Niekum
Minimal Ingredients for Reward Assignment from Expert Demonstrations
Zixuan Dong, Yumi Omori, and Keith W. Ross
Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
Ziyi Liu and Grace Zhang
Q-Based Variational Inverse Reinforcement Learning
Ondrej Bajgar, Peter Tisnikar, Konstantinos Gatsis, Alessandro Abate, and Michael A Osborne
Extending Differential Temporal Difference Methods for Episodic Problems
Kris De Asis, Mohamed Elsayed, and Jiamin He
Revisiting Adam for Streaming Reinforcement Learning
Florin Gogianu, Luțu Adrian-Cătălin, and Razvan Pascanu
Forager: a lightweight testbed for continual learning with partial observability in RL
Steven Tang, Xinze Xiong, Anna Hakhverdyan, Andrew Patterson, Jacob Adkins, Jiamin He, Esraa Elelimy, Parham Mohammad Panahi, Martha White, and Adam White
Dense and Diverse Goal Coverage in Multi Goal Reinforcement Learning
Sagalpreet Singh, Rishi Saket, and Aravindan Raghuveer
Maximum-Entropy Exploration with Future State-Action Visitation Measures
Adrien Bolland, Gaspard Lambrechts, and Damien Ernst
Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs
Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan, and Wendelin Boehmer
Weight a moment: risk-aware exploration with Bayes
Karim Zaghw, Peter Dayan, and Georgy Antonov
A Value-Based Approach to Maximum Entropy Exploration
Jacob Adamczyk, Adam Kamoski, and Rahul V Kulkarni
An Agent-Centric Dynamical Systems Perspective on Multi-Agent Reinforcement Learning
James Rudd-Jones, Maria Perez-Ortiz, and Mirco Musolesi
ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning
Anurag Akula, Satheesh K Perepu, Abhishek Sarkar, and Kaushik Dey
A Causality-Inspired Spatial-Temporal Return Decomposition Approach for Multi-Agent Reinforcement Learning
Yudi Zhang, Yali Du, Biwei Huang, Mykola Pechenizkiy, and Meng Fang
SCoUT: Scalable Communication via Utility-Guided Temporal Grouping in Multi-Agent Reinforcement Learning
Manav Vora, Gokul Puthumanaillam, Hiroyasu Tsukamoto, and Melkior Ornik
Decentralized Asymmetric DQN: Decentralization without Factorization in Multi-Agent Reinforcement Learning
Rupali Bhati, Anurag Kadkol, Andrea Baisero, and Christopher Amato
Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning
Andrea Baisero, Rupali Bhati, Shuo Liu, Aathira Sunil Pillai, and Christopher Amato
Learning Communication Skills in Multi-task Multi-agent Deep Reinforcement Learning
Changxi Zhu, Mehdi Dastani, and Shihan Wang
ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning
Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara, Scott Sanner, Pascal Poupart, Jongmin Lee, and Kee-Eung Kim