Learning Generalized Reactive Policies using Deep Neural Networks

We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach significantly reduces the dependence of learning on handcrafted domain knowledge or feature selection. Instead, the GRP is trained from scratch using a set of successful execution traces. We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and show that our approach facilitates learning complex decision making policies and powerful heuristic functions with minimal human input. Videos of our results are available at goo.gl/Hpy4e3

Intrinsic Motivation Systems for Autonomous Mental Development

Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology. Key words: Active learning, autonomy, behavior, complexity, curiosity, development, developmental trajectory, epigenetic robotics, intrinsic motivation, learning, reinforcement learning, values

Unsupervised representation learning in interactive environments

Extraire une représentation de tous les facteurs de haut niveau de l'état d'un agent à partir d'informations sensorielles de bas niveau est une tâche importante, mais difficile, dans l'apprentissage automatique. Dans ce memoire, nous explorerons plusieurs approches non supervisées pour apprendre ces représentations. Nous appliquons et analysons des méthodes d'apprentissage de représentations non supervisées existantes dans des environnements d'apprentissage par renforcement, et nous apportons notre propre suite d'évaluations et notre propre méthode novatrice d'apprentissage de représentations d'état. Dans le premier chapitre de ce travail, nous passerons en revue et motiverons l'apprentissage non supervisé de représentations pour l'apprentissage automatique en général et pour l'apprentissage par renforcement. Nous introduirons ensuite un sous-domaine relativement nouveau de l'apprentissage de représentations : l'apprentissage auto-supervisé. Nous aborderons ensuite deux approches fondamentales de l'apprentissage de représentations, les méthodes génératives et les méthodes discriminatives. Plus précisément, nous nous concentrerons sur une collection de méthodes discriminantes d'apprentissage de représentations, appelées méthodes contrastives d'apprentissage de représentations non supervisées (CURL). Nous terminerons le premier chapitre en détaillant diverses approches pour évaluer l'utilité des représentations. Dans le deuxième chapitre, nous présenterons un article de workshop dans lequel nous évaluons un ensemble de méthodes d'auto-supervision standards pour les problèmes d'apprentissage par renforcement. Nous découvrons que la performance de ces représentations dépend fortement de la dynamique et de la structure de l'environnement. À ce titre, nous déterminons qu'une étude plus systématique des environnements et des méthodes est nécessaire. Notre troisième chapitre couvre notre deuxième article, Unsupervised State Representation Learning in Atari, où nous essayons d'effectuer une étude plus approfondie des méthodes d'apprentissage de représentations en apprentissage par renforcement, comme expliqué dans le deuxième chapitre. Pour faciliter une évaluation plus approfondie des représentations en apprentissage par renforcement, nous introduisons une suite de 22 jeux Atari entièrement labellisés. De plus, nous choisissons de comparer les méthodes d'apprentissage de représentations de façon plus systématique, en nous concentrant sur une comparaison entre méthodes génératives et méthodes contrastives, plutôt que les méthodes générales du deuxième chapitre choisies de façon moins systématique. Enfin, nous introduisons une nouvelle méthode contrastive, ST-DIM, qui excelle sur ces 22 jeux Atari.Extracting a representation of all the high-level factors of an agent’s state from level-level sensory information is an important, but challenging task in machine learning. In this thesis, we will explore several unsupervised approaches for learning these state representations. We apply and analyze existing unsupervised representation learning methods in reinforcement learning environments, as well as contribute our own evaluation benchmark and our own novel state representation learning method. In the first chapter, we will overview and motivate unsupervised representation learning for machine learning in general and for reinforcement learning. We will then introduce a relatively new subfield of representation learning: self-supervised learning. We will then cover two core representation learning approaches, generative methods and discriminative methods. Specifically, we will focus on a collection of discriminative representation learning methods called contrastive unsupervised representation learning (CURL) methods. We will close the first chapter by detailing various approaches for evaluating the usefulness of representations. In the second chapter, we will present a workshop paper, where we evaluate a handful of off-the-shelf self-supervised methods in reinforcement learning problems. We discover that the performance of these representations depends heavily on the dynamics and visual structure of the environment. As such, we determine that a more systematic study of environments and methods is required. Our third chapter covers our second article, Unsupervised State Representation Learning in Atari, where we try to execute a more thorough study of representation learning methods in RL as motivated by the second chapter. To facilitate a more thorough evaluation of representations in RL we introduce a benchmark of 22 fully labelled Atari games. In addition, we choose the representation learning methods for comparison in a more systematic way by focusing on comparing generative methods with contrastive methods, instead of the less systematically chosen off-the-shelf methods from the second chapter. Finally, we introduce a new contrastive method, ST-DIM, which excels at the 22 Atari games

Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure

Visual system identification: learning physical parameters and latent spaces from pixels

In this thesis, we develop machine learning systems that are able to leverage the knowledge of equations of motion (scene-specific or scene-agnostic) to perform object discovery, physical parameter estimation, position and velocity estimation, camera pose estimation, and learn structured latent spaces that satisfy physical dynamics rules. These systems are unsupervised, learning from unlabelled videos, and use as inductive biases the general equations of motion followed by objects of interest in the scene. This is an important task as in many complex real world environments ground-truth states are not available, although there is physical knowledge of the underlying system. Our goals with this approach, i.e. integration of physics knowledge with unsupervised learning models, are to improve vision-based prediction, enable new forms of control, increase data-efficiency and provide model interpretability, all of which are key areas of interest in machine learning. With the above goals in mind, we start by asking the following question: given a scene in which the objects’ motions are known up to some physical parameters (e.g. a ball bouncing off the floor with unknown restitution coefficient), how do we build a model that uses such knowledge to discover the objects in the scene and estimate these physical parameters? Our first model, PAIG (Physics-as-Inverse-Graphics), approaches this problem from a vision-as-inverse-graphics perspective, describing the visual scene as a composition of objects defined by their location and appearance, which are rendered onto the frame in a graphics manner. This is a known approach in the unsupervised learning literature, where the fundamental problem then becomes that of derendering, that is, inferring and discovering these locations and appearances for each object. In PAIG we introduce a key rendering component, the Coordinate-Consistent Decoder, which enables the integration of the known equations of motion with an inverse-graphics autoencoder architecture (trainable end-to-end), to perform simultaneous object discovery and physical parameter estimation. Although trained on simple simulated 2D scenes, we show that knowledge of the physical equations of motion of the objects in the scene can be used to greatly improve future prediction and provide physical scene interpretability. Our second model, V-SysId, tackles the limitations shown by the PAIG architecture, namely the training difficulty, the restriction to simulated 2D scenes, and the need for noiseless scenes without distractors. Here, we approach the problem from rst principles by asking the question: are neural networks a necessary component to solve this problem? Can we use simpler ideas from classical computer vision instead? With V- SysId, we approach the problem of object discovery and physical parameter estimation from a keypoint extraction, tracking and selection perspective, composed of 3 separate stages: proposal keypoint extraction and tracking, 3D equation tting and camera pose estimation from 2D trajectories, and entropy-based trajectory selection. Since all the stages use lightweight algorithms and optimisers, V-SysId is able to perform joint object discovery, physical parameter and camera pose estimation from even a single video, drastically improving data-efficiency. Additionally, due to the fact that it does not use a rendering/derendering approach, it can be used in real 3D scenes with many distractor objects. We show that this approach enables a number of interest applications, such as vision-based robot end-effector localisation and remote breath rate measurement. Finally, we move into the area of structured recurrent variational models from vision, where we are motivated by the following observation: in existing models, applying a force in the direction from a start point and an end point (in latent space), does not result in a movement from the start point towards the end point, even on the simplest unconstrained environments. This means that the latent space learned by these models does not follow Newton’s law, where the acceleration vector has the same direction as the force vector (in point-mass systems), and prevents the use of PID controllers, which are the simplest and most well understood type of controller. We solve this problem by building inductive biases from Newtonian physics into the latent variable model, which we call NewtonianVAE. Crucially, Newtonian correctness in the latent space brings about the ability to perform proportional (or PID) control, as opposed to the more computationally expensive model predictive control (MPC). PID controllers are ubiquitous in industrial applications, but had thus far lacked integration with unsupervised vision models. We show that the NewtonianVAE learns physically correct latent spaces in simulated 2D and 3D control systems, which can be used to perform goal-based discovery and control in imitation learning, and path following via Dynamic Motion Primitives

Habits and goals in synergy: a variational Bayesian framework for behavior

How to behave efficiently and flexibly is a central problem for understanding biological agents and creating intelligent embodied AI. It has been well known that behavior can be classified as two types: reward-maximizing habitual behavior, which is fast while inflexible; and goal-directed behavior, which is flexible while slow. Conventionally, habitual and goal-directed behaviors are considered handled by two distinct systems in the brain. Here, we propose to bridge the gap between the two behaviors, drawing on the principles of variational Bayesian theory. We incorporate both behaviors in one framework by introducing a Bayesian latent variable called "intention". The habitual behavior is generated by using prior distribution of intention, which is goal-less; and the goal-directed behavior is generated by the posterior distribution of intention, which is conditioned on the goal. Building on this idea, we present a novel Bayesian framework for modeling behaviors. Our proposed framework enables skill sharing between the two kinds of behaviors, and by leveraging the idea of predictive coding, it enables an agent to seamlessly generalize from habitual to goal-directed behavior without requiring additional training. The proposed framework suggests a fresh perspective for cognitive science and embodied AI, highlighting the potential for greater integration between habitual and goal-directed behaviors

Nature-Inspired Inductive Biases in Learning Robots

Die in dieser Dissertation vorgestellten Arbeiten studieren verschiedene von der Natur inspirierte induktive Verzerrungen im Kontext von modellfreiem und modellbasiertem selbstverstärkenden Lernen, mit dem Ziel, KI Agenten zu entwerfen, die effizient und autonom in der realen Welt handeln. Dabei sind von Robotern zu bewältigende Objektmanipulationsaufgaben von besonderem Interesse, da die zeitliche Entwicklung dieser dynamischen Systeme nicht trivial ist und Manipulationsaufgaben schwierige Planungsprobleme darstellen. Die betrachteten induktiven Verzerrungen sind hauptsächlich von in der Natur zu findenden intelligenten Agenten, wie Tiere und Menschen, inspiriert. Die primären Inspirationsquellen sind wie folgt. (1) Hierarchisch organisierte und spezialisierte kortikale Strukturen, die die effektive Erlernung von Fähigkeiten unterstützen. (2) Das selbstorganisierte Spielen von Kindern zum Zwecke der Formung intuitiver Modelle und Theorien über die Welt. (3) Strukturierte Explorationsstrategien basierend auf unterschiedliche Formen von intrinsischer Motivation und lang anhaltender zeitlicher Korrelationen in motorischen Befehlen. (4) Imitationslernen. (5) Die Planung von Aktionssequenzen unter der Berücksichtigung von Unsicherheiten in mentalen Modellen der nichtdeterministischen Welt. Diese Arbeit ist die Fortsetzung einer langen Historie von Ideen und Forschungsbemühungen, die Inspiration aus der Natur ziehen, um kompetentere KI Agenten zu entwickeln. Die Bemühungen in diesen Forschungsfeldern mündeten in der Ausbildung verschiedener Forschungsfelder wie hierarchisches selbstverstärkendes Lernen, Entwicklungsrobotik, intrinsisch motiviertes selbstverstärkendes Lernen und Repräsentationslernen. Diese Arbeit baut auf den in diesen Feldern entwickelten Ideen und Konzepten auf und kombiniert diese mit Methoden von modellfreiem und modellbasiertem selbstverstärkenden Lernen, um es Robotern zu ermöglichen, herausfordernde Objektmanipulationsaufgaben von Grund auf zu lösen. Die Hypothese, dass von der Natur inspirierte induktive Verzerrungen einen essenziellen Beitrag zur Erschaffung kompetenterer KI Agenten liefern könnten, wird dabei durch zahlreiche empirische Studien unterstützt.The work presented in this thesis studies various nature-inspired inductive biases in the domain of model-free and model-based reinforcement learning with the goal of designing AI agents that act more efficiently and autonomously in natural environments. The domain of robotic manipulation tasks is particularly interesting as it involves non-trivial system dynamics and requires abundant planning and reasoning. The inductive biases under investigation are primarily inspired by intelligent agents found in nature, such as humans and other animals. The primary sources of inspiration are as follows. (1) Hierarchically organized and specialized cortical structures facilitating efficient skills learning. (2) The self-organized playing of children to form intuitive theories and models about the world. (3) Structured exploration strategies based on various forms of intrinsic motivation and long-lasting temporal correlations in motor commands. (4) Imitation Learning. (5) Uncertainty-aware planning of motor commands in imagined models of a non-deterministic world. Consequently, this work continues a long history of ideas and research efforts that take inspiration from nature to build more competent AI agents. These efforts culminated in research fields such as hierarchical reinforcement learning, developmental robotics, intrinsically motivated reinforcement learning, and representation learning. This work builds on the ideas that were advanced in these fields. It combines them with model-free and model-based reinforcement learning methods to solve challenging robotic manipulation tasks from scratch. Empirical studies are carried out to support the hypothesis that nature-inspired inductive biases might be an essential building block in designing more competent AI agents