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Found 4 papers, 1 papers with code
Monte-Carlo Search for an Equilibrium in Dec-POMDPs
Decentralized partially observable Markov decision processes (Dec-POMDPs) formalize the problem of designing individual controllers for a group of collaborative agents under stochastic dynamics and partial observability.
Robust Robot Planning for Human-Robot Collaboration
Based on this, we propose two contributions: 1) an approach to automatically generate an uncertain human behavior (a policy) for each given objective function while accounting for possible robot behaviors; and 2) a robot planning algorithm that is robust to the above-mentioned uncertainties and relies on solving a partially observable Markov decision process (POMDP) obtained by reasoning on a distribution over human behaviors.
Solving infinite-horizon Dec-POMDPs using Finite State Controllers within JESP
This paper looks at solving collaborative planning problems formalized as Decentralized POMDPs (Dec-POMDPs) by searching for Nash equilibria, i. e., situations where each agent's policy is a best response to the other agents' (fixed) policies.
A Variational Time Series Feature Extractor for Action Prediction
Our method is based on variational autoencoders.
