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Found 12 papers, 6 papers with code
Solving Long-run Average Reward Robust MDPs via Stochastic Games
First, we derive new computational complexity bounds for solving long-run average reward polytopic RMDPs, showing for the first time that the threshold decision problem for them is in NP coNP and that they admit a randomized algorithm with sub-exponential expected runtime.
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
We also derive a tighter lower bound compared to previous work on the probability of reach-avoidance implied by a RASM, which is required to find a compositional policy with an acceptable probabilistic threshold for complex tasks with multiple edge policies.
Reachability Poorman Discrete-Bidding Games
We consider, for the first time, {\em poorman discrete-bidding} in which the granularity of the bids is restricted and the higher bid is paid to the bank.
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks
We study the problem of training and certifying adversarially robust quantized neural networks (QNNs).
Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees
We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees.
Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systems
We show that this procedure can also be adapted to formally verifying that, under a given Lipschitz continuous control policy, the stochastic system stabilizes within some stabilizing region with probability~$1$.
Learning Stabilizing Policies in Stochastic Control Systems
In this work, we address the problem of learning provably stable neural network policies for stochastic control systems.
Stability Verification in Stochastic Control Systems via Neural Network Supermartingales
We consider the problem of formally verifying almost-sure (a. s.) asymptotic stability in discrete-time nonlinear stochastic control systems.
Infinite Time Horizon Safety of Bayesian Neural Networks
Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction.
Scalable Verification of Quantized Neural Networks (Technical Report)
In this paper, we show that verifying the bit-exact implementation of quantized neural networks with bit-vector specifications is PSPACE-hard, even though verifying idealized real-valued networks and satisfiability of bit-vector specifications alone are each in NP.
Infinite-Duration All-Pay Bidding Games
In {\em bidding games}, however, the players have budgets, and in each turn, we hold an "auction" (bidding) to determine which player moves the token: both players simultaneously submit bids and the higher bidder moves the token.
Optimizing Expectation with Guarantees in POMDPs (Technical Report)
In this work we go beyond both the "expectation" and "threshold" approaches and consider a "guaranteed payoff optimization (GPO)" problem for POMDPs, where we are given a threshold $t$ and the objective is to find a policy $\sigma$ such that a) each possible outcome of $\sigma$ yields a discounted-sum payoff of at least $t$, and b) the expected discounted-sum payoff of $\sigma$ is optimal (or near-optimal) among all policies satisfying a).
