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Omega-Regular Decision Processes
Regular decision processes (RDPs) are a subclass of non-Markovian decision processes where the transition and reward functions are guarded by some regular property of the past (a lookback).
AGNES: Abstraction-guided Framework for Deep Neural Networks Security
One prominent application is image recognition in autonomous driving, where the correct classification of objects, such as traffic signs, is essential for safe driving.
Omega-Regular Reward Machines
Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success.
Backdoor Mitigation in Deep Neural Networks via Strategic Retraining
Deep Neural Networks (DNN) are becoming increasingly more important in assisted and automated driving.
Recursive Reinforcement Learning
Recursion is the fundamental paradigm to finitely describe potentially infinite objects.
Alternating Good-for-MDP Automata
The surprising answer is that we have to pay significantly less when we instead expand the good-for-MDP property to alternating automata: like the nondeterministic GFM automata obtained from deterministic Rabin automata, the alternating good-for-MDP automata we produce from deterministic Streett automata are bi-linear in the the size of the deterministic automaton and its index, and can therefore be exponentially more succinct than minimal nondeterministic B\"uchi automata.
Mungojerrie: Reinforcement Learning of Linear-Time Objectives
Reinforcement learning synthesizes controllers without prior knowledge of the system.
Model-free Reinforcement Learning for Branching Markov Decision Processes
We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs).
