In this paper, first we generate adversarial agents that exhibit flaws in the agent's policy by presenting moving adversaries. Secondly, We use reward shaping ...
This paper generates adversarial agents that exhibit flaws in the agent's policy by presenting moving adversaries, and uses reward shaping and a modified ...
On-Assessing-The-Safety-of-Reinforcement-Learning-algorithms-Using-Formal-Methods. This code contain files that we use to implement our techniques regarding the ...
In this paper, first we generate adversarial agents that exhibit flaws in the agent's policy by presenting moving adversaries. Secondly,. We use reward shaping ...
This paper introduces verification-preserving model updates, the first approach toward obtaining formal safety guarantees for reinforcement learning in settings ...
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This paper begins an exploration of the interaction be- tween formal verification and safe reinforcement learning by introducing a general scheme for ...
Missing: Assessing | Show results with:Assessing
Dec 20, 2023 · Here, we propose a formal methods approach to reinforcement learning that (i) provides a for- mal specification language that integrates high- ...
Aug 31, 2023 · We introduce a formal specification of reinforcement learning problems and algorithms, with a particular focus on temporal difference methods ...
Missing: Assessing | Show results with:Assessing
Oct 19, 2020 · In this paper, we present a semi-formal verification approach for decision-making tasks, based on interval analysis, that addresses the computational demanding.
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In this paper, we take a step towards a direct formal specification of correctness for reinforcement learning problems (agents) and the learning algorithms ...