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Found 5 papers, 0 papers with code
HSVI-based Online Minimax Strategies for Partially Observable Stochastic Games with Neural Perception Mechanisms
For the partially-informed agent, we propose a continual resolving approach which uses lower bounds, pre-computed offline with heuristic search value iteration (HSVI), instead of opponent counterfactual values.
Partially Observable Stochastic Games with Neural Perception Mechanisms
Stochastic games are a well established model for multi-agent sequential decision making under uncertainty.
Point-based Value Iteration for Neuro-Symbolic POMDPs
This requires functions over continuous-state beliefs, for which we propose a novel piecewise linear and convex representation (P-PWLC) in terms of polyhedra covering the continuous-state space and value vectors, and extend Bellman backups to this representation.
Strategy Synthesis for Zero-Sum Neuro-Symbolic Concurrent Stochastic Games
Second, we introduce two novel representations for the value functions and strategies, constant-piecewise-linear (CON-PWL) and constant-piecewise-constant (CON-PWC) respectively, and propose Minimax-action-free PI by extending a recent PI method based on alternating player choices for finite state spaces to Borel state spaces, which does not require normal-form games to be solved.
Verification and Control of Partially Observable Probabilistic Real-Time Systems
We then propose techniques to either verify that such a property holds or to synthesise a controller for the model which makes it true.
Logic in Computer Science Systems and Control
