Search Results for author: Alessandro Abate

Found 23 papers, 13 papers with code

Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise

no code implementations25 Oct 2021 Thom S. Badings, Alessandro Abate, Nils Jansen, David Parker, Hasan A. Poonawala, Marielle Stoelinga

We use state-of-the-art verification techniques to provide guarantees on the iMDP, and compute a controller for which these guarantees carry over to the autonomous system.

Certification of Iterative Predictions in Bayesian Neural Networks

1 code implementation21 May 2021 Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska

We consider the problem of computing reach-avoid probabilities for iterative predictions made with Bayesian neural network (BNN) models.

Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic

1 code implementation24 Feb 2021 Mingyu Cai, Mohammadhosein Hasanbeig, Shaoping Xiao, Alessandro Abate, Zhen Kan

This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state and action spaces.

Motion Planning OpenAI Gym +1

Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice

1 code implementation9 Feb 2021 Lewis Hammond, James Fox, Tom Everitt, Alessandro Abate, Michael Wooldridge

Multi-agent influence diagrams (MAIDs) are a popular form of graphical model that, for certain classes of games, have been shown to offer key complexity and explainability advantages over traditional extensive form game (EFG) representations.

Multi-Agent Reinforcement Learning with Temporal Logic Specifications

1 code implementation1 Feb 2021 Lewis Hammond, Alessandro Abate, Julian Gutierrez, Michael Wooldridge

In this paper, we study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment, which may exhibit probabilistic behaviour.

Multi-agent Reinforcement Learning Temporal Logic

SafePILCO: a software tool for safe and data-efficient policy synthesis

1 code implementation7 Aug 2020 Kyriakos Polymenakos, Nikitas Rontsis, Alessandro Abate, Stephen Roberts

SafePILCO is a software tool for safe and data-efficient policy search with reinforcement learning.

Automated and Sound Synthesis of Lyapunov Functions with SMT Solvers

no code implementations21 Jul 2020 Daniele Ahmed, Andrea Peruffo, Alessandro Abate

In this paper we employ SMT solvers to soundly synthesise Lyapunov functions that assert the stability of a given dynamical model.

Automated and Formal Synthesis of Neural Barrier Certificates for Dynamical Models

no code implementations7 Jul 2020 Andrea Peruffo, Daniele Ahmed, Alessandro Abate

We introduce an automated, formal, counterexample-based approach to synthesise Barrier Certificates (BC) for the safety verification of continuous and hybrid dynamical models.

Jump Operator Planning: Goal-Conditioned Policy Ensembles and Zero-Shot Transfer

no code implementations6 Jul 2020 Thomas J. Ringstrom, Mohammadhosein Hasanbeig, Alessandro Abate

In Hierarchical Control, compositionality, abstraction, and task-transfer are crucial for designing versatile algorithms which can solve a variety of problems with maximal representational reuse.

A Randomized Algorithm to Reduce the Support of Discrete Measures

1 code implementation NeurIPS 2020 Francesco Cosentino, Harald Oberhauser, Alessandro Abate

Given a discrete probability measure supported on $N$ atoms and a set of $n$ real-valued functions, there exists a probability measure that is supported on a subset of $n+1$ of the original $N$ atoms and has the same mean when integrated against each of the $n$ functions.

Carathéodory Sampling for Stochastic Gradient Descent

1 code implementation2 Jun 2020 Francesco Cosentino, Harald Oberhauser, Alessandro Abate

Various flavours of Stochastic Gradient Descent (SGD) replace the expensive summation that computes the full gradient by approximating it with a small sum over a randomly selected subsample of the data set that in turn suffers from a high variance.

Formal Synthesis of Lyapunov Neural Networks

no code implementations19 Mar 2020 Alessandro Abate, Daniele Ahmed, Mirco Giacobbe, Andrea Peruffo

We employ a counterexample-guided approach where a numerical learner and a symbolic verifier interact to construct provably correct Lyapunov neural networks (LNNs).

Cautious Reinforcement Learning with Logical Constraints

no code implementations26 Feb 2020 Mohammadhosein Hasanbeig, Alessandro Abate, Daniel Kroening

This paper presents the concept of an adaptive safe padding that forces Reinforcement Learning (RL) to synthesise optimal control policies while ensuring safety during the learning process.

Temporal Logic

DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning

1 code implementation22 Nov 2019 Mohammadhosein Hasanbeig, Natasha Yogananda Jeppu, Alessandro Abate, Tom Melham, Daniel Kroening

This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives.

Hierarchical Reinforcement Learning Montezuma's Revenge +2

Modular Deep Reinforcement Learning with Temporal Logic Specifications

2 code implementations23 Sep 2019 Lim Zun Yuan, Mohammadhosein Hasanbeig, Alessandro Abate, Daniel Kroening

We propose an actor-critic, model-free, and online Reinforcement Learning (RL) framework for continuous-state continuous-action Markov Decision Processes (MDPs) when the reward is highly sparse but encompasses a high-level temporal structure.

Temporal Logic

Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees

1 code implementation11 Sep 2019 Mohammadhosein Hasanbeig, Yiannis Kantaros, Alessandro Abate, Daniel Kroening, George J. Pappas, Insup Lee

Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics, and biology.

Decision Making Decision Making Under Uncertainty +3

Certified Reinforcement Learning with Logic Guidance

1 code implementation2 Feb 2019 Mohammadhosein Hasanbeig, Alessandro Abate, Daniel Kroening

This probability (certificate) is also calculated in parallel with policy learning when the state space of the MDP is finite: as such, the RL algorithm produces a policy that is certified with respect to the property.

Decision Making Decision Making Under Uncertainty +3

Logically-Constrained Neural Fitted Q-Iteration

no code implementations20 Sep 2018 Mohammadhosein Hasanbeig, Alessandro Abate, Daniel Kroening

We propose a method for efficient training of Q-functions for continuous-state Markov Decision Processes (MDPs) such that the traces of the resulting policies satisfy a given Linear Temporal Logic (LTL) property.

Temporal Logic

Logically-Constrained Reinforcement Learning

1 code implementation24 Jan 2018 Mohammadhosein Hasanbeig, Alessandro Abate, Daniel Kroening

With this reward function, the policy synthesis procedure is "constrained" by the given specification.

Decision Making Decision Making Under Uncertainty +3

Safe Policy Search with Gaussian Process Models

1 code implementation15 Dec 2017 Kyriakos Polymenakos, Alessandro Abate, Stephen Roberts

We propose a method to optimise the parameters of a policy which will be used to safely perform a given task in a data-efficient manner.

Automated Experiment Design for Data-Efficient Verification of Parametric Markov Decision Processes

no code implementations5 Jul 2017 Elizabeth Polgreen, Viraj Wijesuriya, Sofie Haesaert, Alessandro Abate

We present a new method for statistical verification of quantitative properties over a partially unknown system with actions, utilising a parameterised model (in this work, a parametric Markov decision process) and data collected from experiments performed on the underlying system.

Sampling-based Approximations with Quantitative Performance for the Probabilistic Reach-Avoid Problem over General Markov Processes

no code implementations1 Sep 2014 Sofie Haesaert, Robert Babuska, Alessandro Abate

This article deals with stochastic processes endowed with the Markov (memoryless) property and evolving over general (uncountable) state spaces.

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