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

Ensemble Reinforcement Learning: A Survey

Reinforcement Learning (RL) has emerged as a highly effective technique for addressing various scientific and applied problems. Despite its success, certain complex tasks remain challenging to be addressed solely with a single model and algorithm. In response, ensemble reinforcement learning (ERL), a promising approach that combines the benefits of both RL and ensemble learning (EL), has gained widespread popularity. ERL leverages multiple models or training algorithms to comprehensively explore the problem space and possesses strong generalization capabilities. In this study, we present a comprehensive survey on ERL to provide readers with an overview of recent advances and challenges in the field. First, we introduce the background and motivation for ERL. Second, we analyze in detail the strategies that have been successfully applied in ERL, including model averaging, model selection, and model combination. Subsequently, we summarize the datasets and analyze algorithms used in relevant studies. Finally, we outline several open questions and discuss future research directions of ERL. By providing a guide for future scientific research and engineering applications, this survey contributes to the advancement of ERL.Comment: 42 page

Computational approaches for voltage stability monitoring and control in power systems

The electric power grid is a complex, non-linear, non-stationary system comprising of thousands of components such as generators, transformers, transmission lines and advanced power electronics based control devices, and customer loads. The complexity of the grid has been further increased by the introduction of smart grid technologies. Smart grid technology adds to the traditional power grids advanced methods of communication, computation and control as well as increased use of renewable energy sources such as wind and solar farms and a higher penetration of plug-in electric vehicles among others. The smart grid has resulted in much more distributed generation, bi-directional powerflows between customers and the grid, and the semi-autonomous control of subsystems. Due to this added complexity of the grid and the need to maintain reliable, quality, efficient, economical, and environmentally friendly power supply, advanced monitoring and control technologies are needed for real-time operation of various systems that integrate into the transmission and distribution network. In this dissertation, the development of computational intelligence methods for on-line monitoring of voltage stability in a power system is presented. In order to carry out on-line assessment of voltage stability, data from Phasor Measurement Units (PMUs) is utilized. An intelligent algorithm for optimal location of PMUs for voltage stability monitoring is developed. PMU information is used for estimation of voltage stability load index in a power system with plug-in electric vehicle and wind farm included. The estimated voltage stability index is applied in the development of an adaptive dynamic programming based optimal secondary voltage controller to coordinate the reactive power capability of two FACTS devices --Abstract, page iii

Artificial Development by Reinforcement Learning Can Benefit From Multiple Motivations

Research on artificial development, reinforcement learning, and intrinsic motivations like curiosity could profit from the recently developed framework of multi-objective reinforcement learning. The combination of these ideas may lead to more realistic artificial models for life-long learning and goal directed behavior in animals and humans

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

Grounding the Meanings in Sensorimotor Behavior using Reinforcement Learning

The recent outburst of interest in cognitive developmental robotics is fueled by the ambition to propose ecologically plausible mechanisms of how, among other things, a learning agent/robot could ground linguistic meanings in its sensorimotor behavior. Along this stream, we propose a model that allows the simulated iCub robot to learn the meanings of actions (point, touch, and push) oriented toward objects in robot’s peripersonal space. In our experiments, the iCub learns to execute motor actions and comment on them. Architecturally, the model is composed of three neural-network-based modules that are trained in different ways. The first module, a two-layer perceptron, is trained by back-propagation to attend to the target position in the visual scene, given the low-level visual information and the feature-based target information. The second module, having the form of an actor-critic architecture, is the most distinguishing part of our model, and is trained by a continuous version of reinforcement learning to execute actions as sequences, based on a linguistic command. The third module, an echo-state network, is trained to provide the linguistic description of the executed actions. The trained model generalizes well in case of novel action-target combinations with randomized initial arm positions. It can also promptly adapt its behavior if the action/target suddenly changes during motor execution