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Found 8 papers, 0 papers with code
How to Exhibit More Predictable Behaviors
This paper looks at predictability problems, i. e., wherein an agent must choose its strategy in order to optimize the predictions that an external observer could make.
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.
Monte Carlo Information-Oriented Planning
In this article, we discuss how to solve information-gathering problems expressed as rho-POMDPs, an extension of Partially Observable Markov Decision Processes (POMDPs) whose reward rho depends on the belief state.
On Bellman's Optimality Principle for zs-POSGs
Many non-trivial sequential decision-making problems are efficiently solved by relying on Bellman's optimality principle, i. e., exploiting the fact that sub-problems are nested recursively within the original problem.
rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions
In this paper, we focus on POMDPs and ρ-POMDPs with λ ρ -Lipschitz reward function, and demonstrate that, for finite horizons, the optimal value function is Lipschitz-continuous.
A POMDP Extension with Belief-dependent Rewards
Partially Observable Markov Decision Processes (POMDPs) model sequential decision-making problems under uncertainty and partial observability.
