GCRL and MISL are unified as control maximization, with three inequivalent GCRL formulations each matched to a MISL objective via bounds on goal-sensitivity.
Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A distributionally robust safety filter reduces certification for nonlinear systems under arbitrary uncertainties to a one-dimensional switching-time search with Wasserstein-inflated sampling guarantees.
Satisfaction probabilities for homogeneous stochastic MAS under cLTL admit DFA-based tensor decomposition, enabling a dual-tree value iteration framework that reduces redundant dynamic programming computations.
A CMDPST framework combined with LTLf enables synthesis of resource-aware robust strategies for robots facing both probabilistic and nondeterministic uncertainty.
citing papers explorer
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Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
GCRL and MISL are unified as control maximization, with three inequivalent GCRL formulations each matched to a MISL objective via bounds on goal-sensitivity.
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Distributionally Robust Safety Under Arbitrary Uncertainties: A Safety Filtering Approach
A distributionally robust safety filter reduces certification for nonlinear systems under arbitrary uncertainties to a one-dimensional switching-time search with Wasserstein-inflated sampling guarantees.
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Compressing Correct-by-Design Synthesis for Stochastic Homogeneous Multi-Agent Systems with Counting LTL
Satisfaction probabilities for homogeneous stochastic MAS under cLTL admit DFA-based tensor decomposition, enabling a dual-tree value iteration framework that reduces redundant dynamic programming computations.
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Resource-Constrained Robotic Planning in the face of Mixed Uncertainty
A CMDPST framework combined with LTLf enables synthesis of resource-aware robust strategies for robots facing both probabilistic and nondeterministic uncertainty.