Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning

Temporal difference (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor lambda. Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the efficient and general implementation of TD(lambda) for arbitrary lambda, for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suffer from both inefficiency and lack of generality. The TTD (Truncated Temporal Differences) procedure is proposed as an alternative, that indeed only approximates TD(lambda), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using lambda &gt 0 with the TTD procedure allows one to obtain a significant learning speedup at essentially the same cost as usual TD(0) learning.Comment: See http://www.jair.org/ for any accompanying file

A Multiscale Approach for Statistical Characterization of Functional Images

Increasingly, scientific studies yield functional image data, in which the observed data consist of sets of curves recorded on the pixels of the image. Examples include temporal brain response intensities measured by fMRI and NMR frequency spectra measured at each pixel. This article presents a new methodology for improving the characterization of pixels in functional imaging, formulated as a spatial curve clustering problem. Our method operates on curves as a unit. It is nonparametric and involves multiple stages: (i) wavelet thresholding, aggregation, and Neyman truncation to effectively reduce dimensionality; (ii) clustering based on an extended EM algorithm; and (iii) multiscale penalized dyadic partitioning to create a spatial segmentation. We motivate the different stages with theoretical considerations and arguments, and illustrate the overall procedure on simulated and real datasets. Our method appears to offer substantial improvements over monoscale pixel-wise methods. An Appendix which gives some theoretical justifications of the methodology, computer code, documentation and dataset are available in the online supplements

Data-driven methodology for uncertainty quantification of aircraft trajectory predictions

This work present a framework based on datadriven techniques for quantifying and chaos theory for propagating the uncertainty present in the aircraft trajectory prediction process when computing the expected trajectory from a given flight plan. The developed framework employs data assimilation models to capture real-time information from the air traffic system and introduces a novel methodology in order to account for the uncertainty of the weather conditions. The comparison of the resulting set of probabilistic trajectories and the actually flown ones proves how the former could be a key enabler to support envisioned trajectory-based operation concepts and modern airline operations planning.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPostprint (published version

Parsimonious reasoning in reinforcement learning for better credit assignment

Le contenu de cette thèse explore la question de l’attribution de crédits à long terme dans l’apprentissage par renforcement du point de vue d’un biais inductif de parcimonie. Dans ce contexte, un agent parcimonieux cherche à comprendre son environnement en utilisant le moins de variables possible. Autrement dit, si l’agent est crédité ou blâmé pour un certain comportement, la parcimonie l’oblige à attribuer ce crédit (ou blâme) à seulement quelques variables latentes sélectionnées. Avant de proposer de nouvelles méthodes d’attribution parci- monieuse de crédits, nous présentons les travaux antérieurs relatifs à l’attribution de crédits à long terme en relation avec l’idée de sparsité. Ensuite, nous développons deux nouvelles idées pour l’attribution de crédits dans l’apprentissage par renforcement qui sont motivées par un raisonnement parcimonieux : une dans le cadre sans modèle et une pour l’apprentissage basé sur un modèle. Pour ce faire, nous nous appuyons sur divers concepts liés à la parcimonie issus de la causalité, de l’apprentissage supervisé et de la simulation, et nous les appliquons dans un cadre pour la prise de décision séquentielle. La première, appelée évaluation contrefactuelle de la politique, prend en compte les dévi- ations mineures de ce qui aurait pu être compte tenu de ce qui a été. En restreignant l’espace dans lequel l’agent peut raisonner sur les alternatives, l’évaluation contrefactuelle de la politique présente des propriétés de variance favorables à l’évaluation des politiques. L’évaluation contrefactuelle de la politique offre également une nouvelle perspective sur la rétrospection, généralisant les travaux antérieurs sur l’attribution de crédits a posteriori. La deuxième contribution de cette thèse est un algorithme augmenté d’attention latente pour l’apprentissage par renforcement basé sur un modèle : Latent Sparse Attentive Value Gra- dients (LSAVG). En intégrant pleinement l’attention dans la structure d’optimisation de la politique, nous montrons que LSAVG est capable de résoudre des tâches de mémoire active que son homologue sans modèle a été conçu pour traiter, sans recourir à des heuristiques ou à un biais de l’estimateur original.The content of this thesis explores the question of long-term credit assignment in reinforce- ment learning from the perspective of a parsimony inductive bias. In this context, a parsi- monious agent looks to understand its environment through the least amount of variables possible. Alternatively, given some credit or blame for some behavior, parsimony forces the agent to assign this credit (or blame) to only a select few latent variables. Before propos- ing novel methods for parsimonious credit assignment, previous work relating to long-term credit assignment is introduced in relation to the idea of sparsity. Then, we develop two new ideas for credit assignment in reinforcement learning that are motivated by parsimo- nious reasoning: one in the model-free setting, and one for model-based learning. To do so, we build upon various parsimony-related concepts from causality, supervised learning, and simulation, and apply them to the Markov Decision Process framework. The first of which, called counterfactual policy evaluation, considers minor deviations of what could have been given what has been. By restricting the space in which the agent can reason about alternatives, counterfactual policy evaluation is shown to have favorable variance properties for policy evaluation. Counterfactual policy evaluation also offers a new perspective to hindsight, generalizing previous work in hindsight credit assignment. The second contribution of this thesis is a latent attention augmented algorithm for model-based reinforcement learning: Latent Sparse Attentive Value Gradients (LSAVG). By fully inte- grating attention into the structure for policy optimization, we show that LSAVG is able to solve active memory tasks that its model-free counterpart was designed to tackle, without resorting to heuristics or biasing the original estimator

Cognitive Maps

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An integrated theory of language production and comprehension

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal