267 citations · 530 across the 58 of their papers we have counts for
61 papers
Sample-Efficient Policy Space Response Oracles with Joint Experience Best Response
Ariyan Bighashdel, Thiago D. Simão, Frans A. Oliehoek
Multi-agent reinforcement learning (MARL) offers a scalable alternative to exact game-theoretic analysis but suffers from non-stationarity and the need to maintain diverse populati…
Learning to Focus: Prioritizing Informative Histories with Structured Attention Mechanisms in Partially Observable Reinforcement Learning
Daniel De Dios Allegue, Jinke He, Frans A. Oliehoek
Transformers have shown strong ability to model long-term dependencies and are increasingly adopted as world models in model-based reinforcement learning (RL) under partial observa…
Conditional Policy Generator for Dynamic Constraint Satisfaction and Optimization
Wook Lee, Frans A. Oliehoek
Leveraging machine learning methods to solve constraint satisfaction problems has shown promising, but they are mostly limited to a static situation where the problem description i…
Timing the Match: A Deep Reinforcement Learning Approach for Ride-Hailing and Ride-Pooling Services
Yiman Bao, Jie Gao, Jinke He +2
Efficient timing in ride-matching is crucial for improving the performance of ride-hailing and ride-pooling services, as it determines the number of drivers and passengers consider…
SHARPIE: A Modular Framework for Reinforcement Learning and Human-AI Interaction Experiments
Hüseyin Aydın, Kevin Godin-Dubois, Libio Goncalvez Braz +6
Reinforcement learning (RL) offers a general approach for modeling and training AI agents, including human-AI interaction scenarios. In this paper, we propose SHARPIE (Shared Human…
SimuDICE: Offline Policy Optimization Through World Model Updates and DICE Estimation
Catalin E. Brita, Stephan Bongers, Frans A. Oliehoek
In offline reinforcement learning, deriving an effective policy from a pre-collected set of experiences is challenging due to the distribution mismatch between the target policy an…
Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning
Zuzanna Osika, Jazmin Zatarain-Salazar, Frans A. Oliehoek +1
Multi-objective reinforcement learning (MORL) is used to solve problems involving multiple objectives. An MORL agent must make decisions based on the diverse signals provided by di…
Communicating with Speakers and Listeners of Different Pragmatic Levels
Kata Naszadi, Frans A. Oliehoek, Christof Monz
This paper explores the impact of variable pragmatic competence on communicative success through simulating language learning and conversing between speakers and listeners with dif…
Online Planning in POMDPs with State-Requests
Raphael Avalos, Eugenio Bargiacchi, Ann Nowé +2
In key real-world problems, full state information is sometimes available but only at a high cost, like activating precise yet energy-intensive sensors or consulting humans, thereb…
Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory
Mustafa Mert Çelikok, Frans A. Oliehoek, Jan-Willem van de Meent
We consider inverse reinforcement learning problems with concave utilities. Concave Utility Reinforcement Learning (CURL) is a generalisation of the standard RL objective, which em…
Policy Space Response Oracles: A Survey
Ariyan Bighashdel, Yongzhao Wang, Stephen McAleer +2
Game theory provides a mathematical way to study the interaction between multiple decision makers. However, classical game-theoretic analysis is limited in scalability due to the l…
When Do Off-Policy and On-Policy Policy Gradient Methods Align?
Davide Mambelli, Stephan Bongers, Onno Zoeter +2
Policy gradient methods are widely adopted reinforcement learning algorithms for tasks with continuous action spaces. These methods succeeded in many application domains, however,…
What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization
Zuzanna Osika, Jazmin Zatarain Salazar, Diederik M. Roijers +2
We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse…
Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL
Miguel Suau, Matthijs T. J. Spaan, Frans A. Oliehoek
Reinforcement learning agents tend to develop habits that are effective only under specific policies. Following an initial exploration phase where agents try out different actions,…
What model does MuZero learn?
Jinke He, Thomas M. Moerland, Joery A. de Vries +1
Model-based reinforcement learning (MBRL) has drawn considerable interest in recent years, given its promise to improve sample efficiency. Moreover, when using deep-learned models,…
Towards a Unifying Model of Rationality in Multiagent Systems
Robert Loftin, Mustafa Mert Çelikok, Frans A. Oliehoek
Multiagent systems deployed in the real world need to cooperate with other agents (including humans) nearly as effectively as these agents cooperate with one another. To design suc…
Safe Multi-agent Learning via Trapping Regions
Aleksander Czechowski, Frans A. Oliehoek
One of the main challenges of multi-agent learning lies in establishing convergence of the algorithms, as, in general, a collection of individual, self-serving agents is not guaran…
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
Robert Loftin, Mustafa Mert Çelikok, Herke van Hoof +2
In multi-agent problems requiring a high degree of cooperation, success often depends on the ability of the agents to adapt to each other's behavior. A natural solution concept in…
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations
Rolf A. N. Starre, Marco Loog, Elena Congeduti +1
Many methods for Model-based Reinforcement learning (MBRL) in Markov decision processes (MDPs) provide guarantees for both the accuracy of the model they can deliver and the learni…
Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems
Miguel Suau, Jinke He, Mustafa Mert Çelikok +2
Due to its high sample complexity, simulation is, as of today, critical for the successful application of reinforcement learning. Many real-world problems, however, exhibit overly…
On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games
Robert Loftin, Frans A. Oliehoek
Learning to cooperate with other agents is challenging when those agents also possess the ability to adapt to our own behavior. Practical and theoretical approaches to learning in…
Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs
Mustafa Mert Çelikok, Frans A. Oliehoek, Samuel Kaski
Centaurs are half-human, half-AI decision-makers where the AI's goal is to complement the human. To do so, the AI must be able to recognize the goals and constraints of the human a…
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs
Sammie Katt, Hai Nguyen, Frans A. Oliehoek +1
While reinforcement learning (RL) has made great advances in scalability, exploration and partial observability are still active research topics. In contrast, Bayesian RL (BRL) pro…
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems
Miguel Suau, Jinke He, Matthijs T. J. Spaan +1
Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and…