Echo state model of non-Markovian reinforcement learning, An

Department Head: Dale H. Grit.2008 Spring.Includes bibliographical references (pages 137-142).There exists a growing need for intelligent, autonomous control strategies that operate in real-world domains. Theoretically the state-action space must exhibit the Markov property in order for reinforcement learning to be applicable. Empirical evidence, however, suggests that reinforcement learning also applies to domains where the state-action space is approximately Markovian, a requirement for the overwhelming majority of real-world domains. These domains, termed non-Markovian reinforcement learning domains, raise a unique set of practical challenges. The reconstruction dimension required to approximate a Markovian state-space is unknown a priori and can potentially be large. Further, spatial complexity of local function approximation of the reinforcement learning domain grows exponentially with the reconstruction dimension. Parameterized dynamic systems alleviate both embedding length and state-space dimensionality concerns by reconstructing an approximate Markovian state-space via a compact, recurrent representation. Yet this representation extracts a cost; modeling reinforcement learning domains via adaptive, parameterized dynamic systems is characterized by instability, slow-convergence, and high computational or spatial training complexity. The objectives of this research are to demonstrate a stable, convergent, accurate, and scalable model of non-Markovian reinforcement learning domains. These objectives are fulfilled via fixed point analysis of the dynamics underlying the reinforcement learning domain and the Echo State Network, a class of parameterized dynamic system. Understanding models of non-Markovian reinforcement learning domains requires understanding the interactions between learning domains and their models. Fixed point analysis of the Mountain Car Problem reinforcement learning domain, for both local and nonlocal function approximations, suggests a close relationship between the locality of the approximation and the number and severity of bifurcations of the fixed point structure. This research suggests the likely cause of this relationship: reinforcement learning domains exist within a dynamic feature space in which trajectories are analogous to states. The fixed point structure maps dynamic space onto state-space. This explanation suggests two testable hypotheses. Reinforcement learning is sensitive to state-space locality because states cluster as trajectories in time rather than space. Second, models using trajectory-based features should exhibit good modeling performance and few changes in fixed point structure. Analysis of performance of lookup table, feedforward neural network, and Echo State Network (ESN) on the Mountain Car Problem reinforcement learning domain confirm these hypotheses. The ESN is a large, sparse, randomly-generated, unadapted recurrent neural network, which adapts a linear projection of the target domain onto the hidden layer. ESN modeling results on reinforcement learning domains show it achieves performance comparable to lookup table and neural network architectures on the Mountain Car Problem with minimal changes to fixed point structure. Also, the ESN achieves lookup table caliber performance when modeling Acrobot, a four-dimensional control problem, but is less successful modeling the lower dimensional Modified Mountain Car Problem. These performance discrepancies are attributed to the ESN’s excellent ability to represent complex short term dynamics, and its inability to consolidate long temporal dependencies into a static memory. Without memory consolidation, reinforcement learning domains exhibiting attractors with multiple dynamic scales are unlikely to be well-modeled via ESN. To mediate this problem, a simple ESN memory consolidation method is presented and tested for stationary dynamic systems. These results indicate the potential to improve modeling performance in reinforcement learning domains via memory consolidation

Hardware-Efficient Scalable Reinforcement Learning Systems

Reinforcement Learning (RL) is a machine learning discipline in which an agent learns by interacting with its environment. In this paradigm, the agent is required to perceive its state and take actions accordingly. Upon taking each action, a numerical reward is provided by the environment. The goal of the agent is thus to maximize the aggregate rewards it receives over time. Over the past two decades, a large variety of algorithms have been proposed to select actions in order to explore the environment and gradually construct an e¤ective strategy that maximizes the rewards. These RL techniques have been successfully applied to numerous real-world, complex applications including board games and motor control tasks. Almost all RL algorithms involve the estimation of a value function, which indicates how good it is for the agent to be in a given state, in terms of the total expected reward in the long run. Alternatively, the value function may re‡ect on the impact of taking a particular action at a given state. The most fundamental approach for constructing such a value function consists of updating a table that contains a value for each state (or each state-action pair). However, this approach is impractical for large scale problems, in which the state and/or action spaces are large. In order to deal with such problems, it is necessary to exploit the generalization capabilities of non-linear function approximators, such as arti…cial neural networks. This dissertation focuses on practical methodologies for solving reinforcement learning problems with large state and/or action spaces. In particular, the work addresses scenarios in which an agent does not have full knowledge of its state, but rather receives partial information about its environment via sensory-based observations. In order to address such intricate problems, novel solutions for both tabular and function-approximation based RL frameworks are proposed. A resource-efficient recurrent neural network algorithm is presented, which exploits adaptive step-size techniques to improve learning characteristics. Moreover, a consolidated actor-critic network is introduced, which omits the modeling redundancy found in typical actor-critic systems. Pivotal concerns are the scalability and speed of the learning algorithms, for which we devise architectures that map efficiently to hardware. As a result, a high degree of parallelism can be achieved. Simulation results that correspond to relevant testbench problems clearly demonstrate the solid performance attributes of the proposed solutions

Neural combinatorial optimization as an enabler technology to design real-time virtual network function placement decision systems

158 p.The Fifth Generation of the mobile network (5G) represents a breakthrough technology for thetelecommunications industry. 5G provides a unified infrastructure capable of integrating over thesame physical network heterogeneous services with different requirements. This is achieved thanksto the recent advances in network virtualization, specifically in Network Function Virtualization(NFV) and Software Defining Networks (SDN) technologies. This cloud-based architecture not onlybrings new possibilities to vertical sectors but also entails new challenges that have to be solvedaccordingly. In this sense, it enables to automate operations within the infrastructure, allowing toperform network optimization at operational time (e.g., spectrum optimization, service optimization,traffic optimization). Nevertheless, designing optimization algorithms for this purpose entails somedifficulties. Solving the underlying Combinatorial Optimization (CO) problems that these problemspresent is usually intractable due to their NP-Hard nature. In addition, solutions to these problems arerequired in close to real-time due to the tight time requirements on this dynamic environment. Forthis reason, handwritten heuristic algorithms have been widely used in the literature for achievingfast approximate solutions on this context.However, particularizing heuristics to address CO problems can be a daunting task that requiresexpertise. The ability to automate this resolution processes would be of utmost importance forachieving an intelligent network orchestration. In this sense, Artificial Intelligence (AI) is envisionedas the key technology for autonomously inferring intelligent solutions to these problems. Combining AI with network virtualization can truly transform this industry. Particularly, this Thesis aims at using Neural Combinatorial Optimization (NCO) for inferring endsolutions on CO problems. NCO has proven to be able to learn near optimal solutions on classicalcombinatorial problems (e.g., the Traveler Salesman Problem (TSP), Bin Packing Problem (BPP),Vehicle Routing Problem (VRP)). Specifically, NCO relies on Reinforcement Learning (RL) toestimate a Neural Network (NN) model that describes the relation between the space of instances ofthe problem and the solutions for each of them. In other words, this model for a new instance is ableto infer a solution generalizing from the problem space where it has been trained. To this end, duringthe learning process the model takes instances from the learning space, and uses the reward obtainedfrom evaluating the solution to improve its accuracy.The work here presented, contributes to the NCO theory in two main directions. First, this workargues that the performance obtained by sequence-to-sequence models used for NCO in the literatureis improved presenting combinatorial problems as Constrained Markov Decision Processes (CMDP).Such property can be exploited for building a Markovian model that constructs solutionsincrementally based on interactions with the problem. And second, this formulation enables toaddress general constrained combinatorial problems under this framework. In this context, the modelin addition to the reward signal, relies on penalty signals generated from constraint dissatisfactionthat direct the model toward a competitive policy even in highly constrained environments. Thisstrategy allows to extend the number of problems that can be addressed using this technology.The presented approach is validated in the scope of intelligent network management, specifically inthe Virtual Network Function (VNF) placement problem. This problem consists of efficientlymapping a set of network service requests on top of the physical network infrastructure. Particularly,we seek to obtain the optimal placement for a network service chain considering the state of thevirtual environment, so that a specific resource objective is accomplished, in this case theminimization of the overall power consumption. Conducted experiments prove the capability of theproposal for learning competitive solutions when compared to classical heuristic, metaheuristic, andConstraint Programming (CP) solvers

Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning

Low-level control of autonomous underwater vehicles (AUVs) has been extensively addressed by classical control techniques. However, the variable operating conditions and hostile environments faced by AUVs have driven researchers towards the formulation of adaptive control approaches. The reinforcement learning (RL) paradigm is a powerful framework which has been applied in different formulations of adaptive control strategies for AUVs. However, the limitations of RL approaches have lead towards the emergence of deep reinforcement learning which has become an attractive and promising framework for developing real adaptive control strategies to solve complex control problems for autonomous systems. However, most of the existing applications of deep RL use video images to train the decision making artificial agent but obtaining camera images only for an AUV control purpose could be costly in terms of energy consumption. Moreover, the rewards are not easily obtained directly from the video frames. In this work we develop a deep RL framework for adaptive control applications of AUVs based on an actor-critic goal-oriented deep RL architecture, which takes the available raw sensory information as input and as output the continuous control actions which are the low-level commands for the AUV's thrusters. Experiments on a real AUV demonstrate the applicability of the stated deep RL approach for an autonomous robot control problem.Fil: Carlucho, Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Wang, Sen. Heriot-Watt University; Reino UnidoFil: Petillot, Yvan. Heriot-Watt University; Reino UnidoFil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentin

Leveraging deep reinforcement learning in the smart grid environment

L’apprentissage statistique moderne démontre des résultats impressionnants, où les or- dinateurs viennent à atteindre ou même à excéder les standards humains dans certaines applications telles que la vision par ordinateur ou les jeux de stratégie. Pourtant, malgré ces avancées, force est de constater que les applications fiables en déploiement en sont encore à leur état embryonnaire en comparaison aux opportunités qu’elles pourraient apporter. C’est dans cette perspective, avec une emphase mise sur la théorie de décision séquentielle et sur les recherches récentes en apprentissage automatique, que nous démontrons l’applica- tion efficace de ces méthodes sur des cas liés au réseau électrique et à l’optimisation de ses acteurs. Nous considérons ainsi des instances impliquant des unités d’emmagasinement éner- gétique ou des voitures électriques, jusqu’aux contrôles thermiques des bâtiments intelligents. Nous concluons finalement en introduisant une nouvelle approche hybride qui combine les performances modernes de l’apprentissage profond et de l’apprentissage par renforcement au cadre d’application éprouvé de la recherche opérationnelle classique, dans le but de faciliter l’intégration de nouvelles méthodes d’apprentissage statistique sur différentes applications concrètes.While modern statistical learning is achieving impressive results, as computers start exceeding human baselines in some applications like computer vision, or even beating pro- fessional human players at strategy games without any prior knowledge, reliable deployed applications are still in their infancy compared to what these new opportunities could fathom. In this perspective, with a keen focus on sequential decision theory and recent statistical learning research, we demonstrate efficient application of such methods on instances involving the energy grid and the optimization of its actors, from energy storage and electric cars to smart buildings and thermal controls. We conclude by introducing a new hybrid approach combining the modern performance of deep learning and reinforcement learning with the proven application framework of operations research, in the objective of facilitating seamlessly the integration of new statistical learning-oriented methodologies in concrete applications

TRAINING AN AGENT TO MOVE TOWARDS A TARGET INTERACTING WITH A COMPLEX ENVIRONMENT

Στη σημερινή εποχή, το πρόβλημα της αυτόνομης πλοήγησης στα σύγχρονα κινητά ρομπότ αποτελεί σημείο ενδιαφέροντος για την πλειοψηφία της έρευνας που γίνεται γύρω από τη ρομποτική. Αυτό το θέμα γίνεται ακόμα πιο απαιτητικό, καθώς οι απαιτήσεις στα δυναμικά περιβάλλοντα περιλαμβάνουν αυτονομία υψηλού επιπέδου και ευέλικτες δυνατότητες λήψης αποφάσεων για το ρομπότ, ώστε να επιτευχθεί αποφυγής συγκρούσεων. Το Deep learning κατάφερε να λύσει κάποια κοινά ζητήματα στη ρομποτική, όπως η λήψη αποφάσεων, η πλοήγηση και ο έλεγχος, όμως, με εποπτευόμενο τρόπο. Οι Reinforcement learning τεχνολογίες έχουν συνδυαστεί με το Deep learning, με αποτέλεσμα ένα νέο ερευνητικό θέμα γνωστό ως deep reinforcement learning (DRL). Με τη χρήση του DRL, η διαδικασία μπορεί να αυτοματοποιηθεί με τη μετάφραση δεδομένων αισθητήρων πολλών διαστάσεων σε εντολές κίνησης ρομπότ χωρίς τη χρήση κεντρικοποιημένων πληροφοριών, παρέχοντας έναν μη εποπτευόμενο τρόπο. Αυτό που χρειάζεται, για να ενθαρρυνθεί ο agent μάθησης και μέσω διαδικασίας δοκιμής και σφάλματος με το περιβάλλον, να βρει την καλύτερη δράση για κάθε κατάσταση, είναι μία βαθμωτή συνάρτηση ανταμοιβής. Στην εν λόγω διατριβή, δημιουργήθηκε ένα προσομοιωμένο περιβάλλον με ένα κινητό ρομπότ που αλληλεπιδρά με αυτό. Δύο αλγόριθμοι βασισμένοι στο DRL, οι Actor-Critic και PPO, χρησιμοποιήθηκαν για να εκπαιδεύσουν τον παράγοντα να κινείται με ασφάλεια στο περιβάλλον, αποφεύγοντας τα εμπόδια και στοχεύοντας στην επίτευξη ενός καθορισμένου στόχου. Τα αποτελέσματά τους παρουσιάζονται και συγκρίνονται.Nowadays, the problem of autonomous navigation in modern mobile robots is the point of interest for the majority of research in robotics. This topic becomes even more challenging as the requirements in dynamic environments include high-level autonomy and flexible decision-making capabilities for the robot, to achieve collision avoidance. Deep learning has succeeded in solving some common issues in robotics, such as decision making, navigation and control, in a supervised manner though. Reinforcement learning frameworks have been combined with deep learning, resulting in a new research topic known as deep reinforcement learning (DRL). With the use of DRL the procedure can become automated by mapping high-dimensional sensory data to robot motion commands without using ground-truth information, providing an unsupervised manner. It simply takes a scalar reward function to encourage the learning agent through trial-and-error interactions with the environment, with the goal of finding the best action for each state. In the project thesis in question, a simulated environment was created with a mobile robot interacting with it. Two DRL-based algorithms, Actor-Critic and PPO were used to train the agent to move safely in the environment, avoiding the obstacles and aiming to reach a specified goal. Their results are presented and compared

Effective offline training and efficient online adaptation

Developing agents that behave intelligently in the world is an open challenge in machine learning. Desiderata for such agents are efficient exploration, maximizing long term utility, and the ability to effectively leverage prior data to solve new tasks. Reinforcement learning (RL) is an approach that is predicated on learning by directly interacting with an environment through trial-and-error, and presents a way for us to train and deploy such agents. Moreover, combining RL with powerful neural network function approximators – a sub-field known as “deep RL” – has shown evidence towards achieving this goal. For instance, deep RL has yielded agents that can play Go at superhuman levels, improve the efficiency of microchip designs, and learn complex novel strategies for controlling nuclear fusion reactions. A key issue that stands in the way of deploying deep RL is poor sample efficiency. Concretely, while it is possible to train effective agents using deep RL, the key successes have largely been in environments where we have access to large amounts of online interaction, often through the use of simulators. However, in many real-world problems, we are confronted with scenarios where samples are expensive to obtain. As has been alluded to, one way to alleviate this issue is through accessing some prior data, often termed “offline data”, which can accelerate how quickly we learn such agents, such as leveraging exploratory data to prevent redundant deployments, or using human-expert data to quickly guide agents towards promising behaviors and beyond. However, the best way to incorporate this data into existing deep RL algorithms is not straightforward; naïvely pre-training using RL algorithms on this offline data, a paradigm called “offline RL” as a starting point for subsequent learning is often detrimental. Moreover, it is unclear how to explicitly derive useful behaviors online that are positively influenced by this offline pre-training. With these factors in mind, this thesis follows a 3-pronged strategy towards improving sample-efficiency in deep RL. First, we investigate effective pre-training on offline data. Then, we tackle the online problem, looking at efficient adaptation to environments when operating purely online. Finally, we conclude with hybrid strategies that use offline data to explicitly augment policies when acting online

Towards Continual Reinforcement Learning: A Review and Perspectives

In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We begin by discussing our perspective on why RL is a natural fit for studying continual learning. We then provide a taxonomy of different continual RL formulations and mathematically characterize the non-stationary dynamics of each setting. We go on to discuss evaluation of continual RL agents, providing an overview of benchmarks used in the literature and important metrics for understanding agent performance. Finally, we highlight open problems and challenges in bridging the gap between the current state of continual RL and findings in neuroscience. While still in its early days, the study of continual RL has the promise to develop better incremental reinforcement learners that can function in increasingly realistic applications where non-stationarity plays a vital role. These include applications such as those in the fields of healthcare, education, logistics, and robotics.Comment: Preprint, 52 pages, 8 figure