Zobrazeno 1 - 10
of 483
pro vyhledávání: '"ABATE, ALESSANDRO"'
Publikováno v:
Leibniz Transactions on Embedded Systems, Vol 8, Iss 2, Pp 0:i-0:iii (2022)
This special issue contains seven papers within the broad subject of Distributed Hybrid Systems, that is, systems combining hybrid discrete-continuous state spaces with elements of concurrency and logical or spatial distribution. It follows up on sev
Externí odkaz:
https://doaj.org/article/bd6ca138b48f4126a0204751e1b63a03
A crucial capability of Machine Learning models in real-world applications is the ability to continuously learn new tasks. This adaptability allows them to respond to potentially inevitable shifts in the data-generating distribution over time. Howeve
Externí odkaz:
http://arxiv.org/abs/2410.07812
Autor:
Mathiesen, Frederik Baymler, Romao, Licio, Calvert, Simeon C., Laurenti, Luca, Abate, Alessandro
In this paper, we present a novel data-driven approach to quantify safety for non-linear, discrete-time stochastic systems with unknown noise distribution. We define safety as the probability that the system remains in a given region of the state spa
Externí odkaz:
http://arxiv.org/abs/2410.06662
Linear temporal logic (LTL) has recently been adopted as a powerful formalism for specifying complex, temporally extended tasks in reinforcement learning (RL). However, learning policies that efficiently satisfy arbitrary specifications not observed
Externí odkaz:
http://arxiv.org/abs/2410.04631
To evaluate the safety and usefulness of deployment protocols for untrusted AIs, AI Control uses a red-teaming exercise played between a protocol designer and an adversary. This paper introduces AI-Control Games, a formal decision-making model of the
Externí odkaz:
http://arxiv.org/abs/2409.07985
Autor:
Benjamin, Patrick, Abate, Alessandro
Recent works have provided algorithms by which decentralised agents, which may be connected via a communication network, can learn equilibria in Mean-Field Games from a single, non-episodic run of the empirical system. However, these algorithms are g
Externí odkaz:
http://arxiv.org/abs/2408.11607
We present a data-driven approach for producing policies that are provably robust across unknown stochastic environments. Existing approaches can learn models of a single environment as an interval Markov decision processes (IMDP) and produce a robus
Externí odkaz:
http://arxiv.org/abs/2408.03093
The goal of Bayesian inverse reinforcement learning (IRL) is recovering a posterior distribution over reward functions using a set of demonstrations from an expert optimizing for a reward unknown to the learner. The resulting posterior over rewards c
Externí odkaz:
http://arxiv.org/abs/2407.10971
In reinforcement learning, specifying reward functions that capture the intended task can be very challenging. Reward learning aims to address this issue by learning the reward function. However, a learned reward model may have a low error on the tra
Externí odkaz:
http://arxiv.org/abs/2406.15753
Leveraging human preferences for steering the behavior of Large Language Models (LLMs) has demonstrated notable success in recent years. Nonetheless, data selection and labeling are still a bottleneck for these systems, particularly at large scale. H
Externí odkaz:
http://arxiv.org/abs/2406.10023