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Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Optimally solving decentralized partially observable Markov decision processes under either full or no information sharing received significant attention in recent years.
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Optimally solving decentralized partially observable Markov decision processes under either full or no information sharing received significant attention in recent years.
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.
Solving Hierarchical Information-Sharing Dec-POMDPs: An Extensive-Form Game Approach
A recent theory shows that a multi-player decentralized partially observable Markov decision process can be transformed into an equivalent single-player game, enabling the application of \citeauthor{bellman}'s principle of optimality to solve the single-player game by breaking it down into single-stage subgames.
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.
HSVI can solve zero-sum Partially Observable Stochastic Games
State-of-the-art methods for solving 2-player zero-sum imperfect information games rely on linear programming or regret minimization, though not on dynamic programming (DP) or heuristic search (HS), while the latter are often at the core of state-of-the-art solvers for other sequential decision-making problems.
HSVI for zs-POSGs using Concavity, Convexity and Lipschitz Properties
Dynamic programming and heuristic search are at the core of state-of-the-art solvers for sequential decision-making problems.
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.
Reinforcement Learning
Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be efficient in many domains, e. g., board games, video games or autonomous vehicles.
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.
Learning to Act in Decentralized Partially Observable MDPs
We address a long-standing open problem of reinforcement learning in decentralized partially observable Markov decision processes.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
POMDPs Make Better Hackers: Accounting for Uncertainty in Penetration Testing
Penetration Testing is a methodology for assessing network security, by generating and executing possible hacking attacks.
Les POMDP font de meilleurs hackers: Tenir compte de l'incertitude dans les tests de penetration
Penetration Testing is a methodology for assessing network security, by generating and executing possible hacking attacks.
Penetration Testing == POMDP Solving?
Penetration Testing is a methodology for assessing network security, by generating and executing possible attacks.
A POMDP Extension with Belief-dependent Rewards
Partially Observable Markov Decision Processes (POMDPs) model sequential decision-making problems under uncertainty and partial observability.
