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Found 8 papers, 3 papers with code
On Imperfect Recall in Multi-Agent Influence Diagrams
Multi-agent influence diagrams (MAIDs) are a popular game-theoretic model based on Bayesian networks.
Reasoning about Causality in Games
Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support.
Lexicographic Multi-Objective Reinforcement Learning
In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems.
Multi-Objective Reinforcement Learning
reinforcement-learning
Observational Robustness and Invariances in Reinforcement Learning via Lexicographic Objectives
Policy gradient algorithms that have strong convergence guarantees are usually modified to obtain robust policies in ways that do not preserve algorithm guarantees, which defeats the purpose of formal robustness requirements.
Rational Verification for Probabilistic Systems
Rational verification is the problem of determining which temporal logic properties will hold in a multi-agent system, under the assumption that agents in the system act rationally, by choosing strategies that collectively form a game-theoretic equilibrium.
Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice
Multi-agent influence diagrams (MAIDs) are a popular form of graphical model that, for certain classes of games, have been shown to offer key complexity and explainability advantages over traditional extensive form game (EFG) representations.
Multi-Agent Reinforcement Learning with Temporal Logic Specifications
In this paper, we study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment, which may exhibit probabilistic behaviour.
Multi-agent Reinforcement Learning
reinforcement-learning
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Learning Tractable Probabilistic Models for Moral Responsibility and Blame
From the viewpoint of such systems, the urgent questions are: (a) How can models of moral scenarios and blameworthiness be extracted and learnt automatically from data?
