Search Results for author:
Found 43 papers, 18 papers with code
Networked Communication for Decentralised Agents in Mean-Field Games
For comparison purposes with our new architecture, we modify recent algorithms for the centralised and independent cases to make their practical convergence feasible: while contributing the first empirical demonstrations of these algorithms in our setting of $N$ agents learning along a single system evolution with only local state observability, we additionally display the empirical benefits of our new, networked approach.
Model Reduction of Linear Stochastic Systems with Preservation of sc-LTL Specifications
We propose a correct-by-design controller synthesis framework for discrete-time linear stochastic systems that provides more flexibility to the overall abstraction framework of stochastic systems.
Inner approximations of stochastic programs for data-driven stochastic barrier function design
Our approach is based on a novel approach to synthesise a stochastic barrier function from noise data.
Distributionally Robust Optimal and Safe Control of Stochastic Systems via Kernel Conditional Mean Embedding
We develop a distributionally robust framework to perform dynamic programming using kernel methods and apply our framework to design feedback control policies that satisfy safety and optimality specifications.
Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version]
In order to learn the optimal structure, we define a Kantorovich-inspired metric between Markov chains, and we use it as a loss function.
Policy Evaluation in Distributional LQR
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard RL.
Neural Abstractions
We present a novel method for the safety verification of nonlinear dynamical models that uses neural networks to represent abstractions of their dynamics.
Reasoning about Causality in Games
Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support.
Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions
This iMDP is, with a user-specified confidence probability, robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples.
Lexicographic Multi-Objective Reinforcement Learning
In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems.
Multi-Objective Reinforcement Learning
reinforcement-learning
Misspecification in Inverse Reinforcement Learning
In this paper, we provide a mathematical analysis of how robust different IRL models are to misspecification, and answer precisely how the demonstrator policy may differ from each of the standard models before that model leads to faulty inferences about the reward function $R$.
Data-driven memory-dependent abstractions of dynamical systems
We propose a sample-based, sequential method to abstract a (potentially black-box) dynamical system with a sequence of memory-dependent Markov chains of increasing size.
Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics
We consider the cases where the transition probabilities of this MDP are either known up to an interval or completely unknown.
Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty
Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters leads to epistemic uncertainty.
Observational Robustness and Invariances in Reinforcement Learning via Lexicographic Objectives
Policy gradient algorithms that have strong convergence guarantees are usually modified to obtain robust policies in ways that do not preserve algorithm guarantees, which defeats the purpose of formal robustness requirements.
LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learning
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 linear temporal specification with maximal probability.
Learning Task Automata for Reinforcement Learning using Hidden Markov Models
Second, we propose a novel method for distilling the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP.
Low Emission Building Control with Zero-Shot Reinforcement Learning
Heating and cooling systems in buildings account for 31\% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efficiency nor minimise emissions by interacting optimally with the grid.
Low Emission Building Control with Zero-Shot Reinforcement Learning
Heating and cooling systems in buildings account for 31% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efficiency nor minimise emissions by interacting optimally with the grid.
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning
It is often very challenging to manually design reward functions for complex, real-world tasks.
Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise
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
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
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.
Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice
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
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
reinforcement-learning
+1
SafePILCO: a software tool for safe and data-efficient policy synthesis
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
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
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
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
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
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
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
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.
Safety Guarantees for Planning Based on Iterative Gaussian Processes
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty.
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
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.
Modular Deep Reinforcement Learning with Temporal Logic Specifications
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.
Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees
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.
Certified Reinforcement Learning with Logic Guidance
Reinforcement Learning (RL) is a widely employed machine learning architecture that has been applied to a variety of control problems.
Logically-Constrained Neural Fitted Q-Iteration
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.
Logically-Constrained Reinforcement Learning
With this reward function, the policy synthesis procedure is "constrained" by the given specification.
Safe Policy Search with Gaussian Process Models
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
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
This article deals with stochastic processes endowed with the Markov (memoryless) property and evolving over general (uncountable) state spaces.
