Dipl.-Inf.

Roscher, Karsten

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Towards Probabilistic Safety Guarantees for Model-Free Reinforcement Learning

2023 , Schmoeller da Roza, Felippe , Roscher, Karsten , Günneman, Stephan

Improving safety in model-free Reinforcement Learning is necessary if we expect to deploy such systems in safety-critical scenarios. However, most of the existing constrained Reinforcement Learning methods have no formal guarantees for their constraint satisfaction properties. In this paper, we show the theoretical formulation for a safety layer that encapsulates model epistemic uncertainty over a distribution of constraint model approximations and can provide probabilistic guarantees of constraint satisfaction.

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Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models

2023 , Haider, Tom , Roscher, Karsten , Schmoeller da Roza, Felippe , Günnemann, Stephan

Reliability of reinforcement learning (RL) agents is a largely unsolved problem. Especially in situations that substantially differ from their training environment, RL agents often exhibit unpredictable behavior, potentially leading to performance loss, safety violations or catastrophic failure. Reliable decision making agents should therefore be able to cast an alert whenever they encounter situations they have never seen before and do not know how to handle. While the problem, also known as out-of-distribution (OOD) detection, has received considerable attention in other domains such as image classification or sensory data analysis, it is less frequently studied in the context of RL. In fact, there is not even a common understanding of what OOD actually means in RL. In this work, we want to bridge this gap and approach the topic of OOD in RL from a general perspective. For this, we formulate OOD in RL as severe perturbations of the Markov decision process (MDP). To detect such perturbations, we introduce a predictive algorithm utilizing probabilistic dynamics models and bootstrapped ensembles. Since existing benchmarks are sparse and limited in their complexity, we also propose a set of evaluation scenarios with OOD occurrences. A detailed analysis of our approach shows superior detection performance compared to existing baselines from related fields.

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