77 citations · 182 across the 10 of their papers we have counts for
12 papers
LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learning
Hosein Hasanbeig, Daniel Kroening, Alessandro Abate
LCRL is a software tool that implements model-free Reinforcement Learning (RL) algorithms over unknown Markov Decision Processes (MDPs), synthesising policies that satisfy a given…
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic
Mingyu Cai, Mohammadhosein Hasanbeig, Shaoping Xiao +2
This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state a…
Shielding Atari Games with Bounded Prescience
Mirco Giacobbe, Mohammadhosein Hasanbeig, Daniel Kroening +1
Deep reinforcement learning (DRL) is applied in safety-critical domains such as robotics and autonomous driving. It achieves superhuman abilities in many tasks, however whether DRL…
Goal Kernel Planning: Linearly-Solvable Non-Markovian Policies for Logical Tasks with Goal-Conditioned Options
Thomas J. Ringstrom, Mohammadhosein Hasanbeig, Alessandro Abate
In the domain of hierarchical planning, compositionality, abstraction, and task transfer are crucial for designing algorithms that can efficiently solve a variety of problems with…
Cautious Reinforcement Learning with Logical Constraints
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…
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
Mohammadhosein Hasanbeig, Natasha Yogananda Jeppu, Alessandro Abate +2
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…
Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees
Mohammadhosein Hasanbeig, Yiannis Kantaros, Alessandro Abate +3
Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics,…
Modular Deep Reinforcement Learning with Temporal Logic Specifications
Lim Zun Yuan, Mohammadhosein Hasanbeig, Alessandro Abate +1
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…
Certified Reinforcement Learning with Logic Guidance
Hosein Hasanbeig, Daniel Kroening, Alessandro Abate
Reinforcement Learning (RL) is a widely employed machine learning architecture that has been applied to a variety of control problems. However, applications in safety-critical doma…
Logically-Constrained Neural Fitted Q-Iteration
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 Linea…
From Game-theoretic Multi-agent Log Linear Learning to Reinforcement Learning
Mohammadhosein Hasanbeig, Lacra Pavel
The main focus of this paper is on enhancement of two types of game-theoretic learning algorithms: log-linear learning and reinforcement learning. The standard analysis of log-line…
Logically-Constrained Reinforcement Learning
Mohammadhosein Hasanbeig, Alessandro Abate, Daniel Kroening
We present the first model-free Reinforcement Learning (RL) algorithm to synthesise policies for an unknown Markov Decision Process (MDP), such that a linear time property is satis…