30 citations · 115 across the 26 of their papers we have counts for
28 papers
On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners
David Mguni, Julian Ma, Jun Wang
Large Language Models (LLMs) are frequently portrayed as general-purpose solvers capable of solving arbitrary tasks. We argue that this view overlooks a fundamental constraint: lan…
On the Geometry of Games and their Solvers
Yaqi Sun, Julian Ma, David Mguni
A central challenge in game theory and learning systems such as GANs is understanding which algorithms can efficiently compute equilibria across the heterogeneous landscape of game…
Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints
David Mguni, Yaqi Sun, Haojun Chen +4
We study robustness to agent malfunctions in cooperative multi-agent reinforcement learning (MARL), a failure mode that is critical in practice yet underexplored in existing theory…
All Language Models Large and Small
Zhixun Chen, Yali Du, David Mguni
Many leading language models (LMs) use high-intensity computational resources both during training and execution. This poses the challenge of lowering resource costs for deployment…
A survey on algorithms for Nash equilibria in finite normal-form games
Hanyu Li, Wenhan Huang, Zhijian Duan +4
Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing deman…
Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models
Xue Yan, Yan Song, Xinyu Cui +4
Large language models (LLMs) demonstrate their promise in tackling complicated practical challenges by combining action-based policies with chain of thought (CoT) reasoning. Having…
ChessGPT: Bridging Policy Learning and Language Modeling
Xidong Feng, Yicheng Luo, Ziyan Wang +6
When solving decision-making tasks, humans typically depend on information from two key sources: (1) Historical policy data, which provides interaction replay from the environment,…
MANSA: Learning Fast and Slow in Multi-Agent Systems
David Mguni, Haojun Chen, Taher Jafferjee +7
In multi-agent reinforcement learning (MARL), independent learning (IL) often shows remarkable performance and easily scales with the number of agents. Yet, using IL can be ineffic…
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
Lukas Schäfer, Oliver Slumbers, Stephen McAleer +3
Multi-agent reinforcement learning (MARL) requires agents to explore within a vast joint action space to find joint actions that lead to coordination. Existing value-based MARL alg…
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction
Taher Jafferjee, Juliusz Ziomek, Tianpei Yang +6
Centralised training with decentralised execution (CT-DE) serves as the foundation of many leading multi-agent reinforcement learning (MARL) algorithms. Despite its popularity, it…
SEREN: Knowing When to Explore and When to Exploit
Changmin Yu, David Mguni, Dong Li +3
Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states. To enc…
On the Convergence of Fictitious Play: A Decomposition Approach
Yurong Chen, Xiaotie Deng, Chenchen Li +4
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in -player games, which builds the foundation for modern…
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints
David Mguni, Aivar Sootla, Juliusz Ziomek +4
Many real-world settings involve costs for performing actions; transaction costs in financial systems and fuel costs being common examples. In these settings, performing actions at…
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
Oliver Slumbers, David Henry Mguni, Stephen Marcus McAleer +3
In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other agents. However, the dominant paradigm in game t…
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee +4
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications. For example, plan…
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
David Henry Mguni, Taher Jafferjee, Jianhong Wang +7
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to joint…
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
David Mguni, Usman Islam, Yaqi Sun +7
Reinforcement learning (RL) involves performing exploratory actions in an unknown system. This can place a learning agent in dangerous and potentially catastrophic system states. C…
Online Markov Decision Processes with Non-oblivious Strategic Adversary
Le Cong Dinh, David Henry Mguni, Long Tran-Thanh +2
We study a novel setting in Online Markov Decision Processes (OMDPs) where the loss function is chosen by a non-oblivious strategic adversary who follows a no-external regret algor…
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games
Xiaotie Deng, Ningyuan Li, David Mguni +2
Similar to the role of Markov decision processes in reinforcement learning, Stochastic Games (SGs) lay the foundation for the study of multi-agent reinforcement learning (MARL) and…
Settling the Variance of Multi-Agent Policy Gradients
Jakub Grudzien Kuba, Muning Wen, Yaodong Yang +5
Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL (MARL…
Learning in Nonzero-Sum Stochastic Games with Potentials
David Mguni, Yutong Wu, Yali Du +6
Multi-agent reinforcement learning (MARL) has become effective in tackling discrete cooperative game scenarios. However, MARL has yet to penetrate settings beyond those modelled by…
Learning to Shape Rewards using a Game of Two Partners
David Mguni, Taher Jafferjee, Jianhong Wang +9
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. However, RS typically relies on manually engi…
Modelling Behavioural Diversity for Learning in Open-Ended Games
Nicolas Perez Nieves, Yaodong Yang, Oliver Slumbers +3
Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.g., Rock-Paper-Scissor…
Online Double Oracle
Le Cong Dinh, Yaodong Yang, Stephen McAleer +6
Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence. This paper proposes new learnin…