Motivated Learning for Computational Intelligence

2012 ◽  
pp. 120-146 ◽  
Author(s):  
Janusz A. Starzyk
Keyword(s):  
Reinforcement Learning ◽  
Motor Skills ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Model Building ◽  
Value Functions ◽  
Changing Environments ◽  
Learning Techniques ◽  
Sensory Motor ◽  
External Reward

This chapter describes a motivated learning (ML) method that advances model building and learning techniques required for intelligent systems. Motivated learning addresses critical limitations of reinforcement learning (RL), the more common approach to coordinating a machine’s interaction with an unknown environment. RL maximizes the external reward by approximating multidimensional value functions; however, it does not work well in dynamically changing environments. The ML method overcomes RL problems by triggering internal motivations, and creating abstract goals and internal reward systems to stimulate learning. The chapter addresses the important question of how to motivate an agent to learn and enhance its own complexity? A mechanism is presented that extends low-level sensory-motor interactions towards advanced perception and motor skills, resulting in the emergence of desired cognitive properties. ML is compared to RL using a rapidly changing environment in which the agent needs to manage its motivations as well as choose and implement goals in order to succeed.

Motivated Learning for Computational Intelligence

2011 ◽  
pp. 265-292 ◽  
Author(s):  
Janusz A. Starzyk
Keyword(s):  
Reinforcement Learning ◽  
Motor Skills ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Model Building ◽  
Value Functions ◽  
Changing Environments ◽  
Learning Techniques ◽  
Sensory Motor ◽  
External Reward

This chapter describes a motivated learning (ML) method that advances model building and learning techniques required for intelligent systems. Motivated learning addresses critical limitations of reinforcement learning (RL), the more common approach to coordinating a machine’s interaction with an unknown environment. RL maximizes the external reward by approximating multidimensional value functions; however, it does not work well in dynamically changing environments. The ML method overcomes RL problems by triggering internal motivations, and creating abstract goals and internal reward systems to stimulate learning. The chapter addresses the important question of how to motivate an agent to learn and enhance its own complexity? A mechanism is presented that extends low-level sensory-motor interactions towards advanced perception and motor skills, resulting in the emergence of desired cognitive properties. ML is compared to RL using a rapidly changing environment in which the agent needs to manage its motivations as well as choose and implement goals in order to succeed.

scholarly journals Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Jacopo Castellini ◽  
Frans A. Oliehoek ◽  
Rahul Savani ◽  
Shimon Whiteson
Keyword(s):  
Reinforcement Learning ◽  
Multiagent Systems ◽  
Autonomous Agents ◽  
Coordination Mechanism ◽  
Network Architectures ◽  
Value Functions ◽  
Multi Agent Systems ◽  
Learning Techniques ◽  
Coordination Requirements ◽  
Multi Agent

AbstractRecent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theoretical insight, it remains unclear what the employed neural networks are learning, or how we should enhance their learning power to address the problems on which they fail. In this work, we empirically investigate the learning power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial problems that arise in the multi-agent setting, such as an exponential number of joint actions or the lack of an explicit coordination mechanism. Our results extend those in Castellini et al. (Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS’19.International Foundation for Autonomous Agents and Multiagent Systems, pp 1862–1864, 2019) and quantify how well various approaches can represent the requisite value functions, and help us identify the reasons that can impede good performance, like sparsity of the values or too tight coordination requirements.

scholarly journals A Framework for Object Detection using Deep-Reinforcement Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1508-1514
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Object Detection ◽  
Intelligent Systems ◽  
Learning Algorithm ◽  
Efficient Tool ◽  
Narrow Scope ◽  
Deep Learning Algorithm ◽  
Learning Techniques

Machine learning area enable the utilization of Deep learning algorithm and neural networks (DNNs) with Reinforcement Learning. Reinforcement learning and DL both is region of AI, it’s an efficient tool towards structuring artificially intelligent systems and solving sequential deciding problems. Reinforcement learning (RL) deals with the history of moves; Reinforcement learning problems are often resolve by an agent often denoted as (A) it has privilege to make decisions during a situation to optimize a given problem by collective rewards. Ability to structure sizable amount of attributes make deep learning an efficient tool for unstructured data. Comparing multiple deep learning algorithms may be a major issue thanks to the character of the training process and therefore the narrow scope of datasets tested in algorithmic prisons. Our research proposed a framework which exposed that reinforcement learning techniques in combination with Deep learning techniques learn functional representations for sorting problems with high dimensional unprocessed data. The faster RCNN model typically founds objects in faster way saving resources like computation, processing, and storage. But still object detection technique typically require high computation power and large memory and processor building it hard to run on resource constrained devices (RCD) for detecting an object during real time without an efficient and high computing machine.

Relationship Between Sensory--Motor Skills and Cognitive Processing in ADHD

2006 ◽  
Author(s):  
Andrew S. Davis ◽  
Lisa A. Pass ◽  
W. Holmes Finch ◽  
Raymond S. Dean ◽  
Richard W. Woodcock
Keyword(s):  
Cognitive Processing ◽  
Motor Skills ◽  
Sensory Motor

scholarly journals Optimal Policies for Quantum Markov Decision Processes

2021 ◽  
Author(s):  
Ming-Sheng Ying ◽  
Yuan Feng ◽  
Sheng-Gang Ying
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Quantum Systems ◽  
Sequential Decision Making ◽  
Mathematical Framework ◽  
Sequential Decision ◽  
Learning Techniques ◽  
Optimal Policies ◽  
Markov Decision ◽  
Programming Algorithms

AbstractMarkov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

scholarly journals On collaborative reinforcement learning to optimize the redistribution of critical medical supplies throughout the COVID-19 pandemic

2020 ◽  
Author(s):  
Bryan P Bednarski ◽  
Akash Deep Singh ◽  
William M Jones
Keyword(s):  
Public Health ◽  
Reinforcement Learning ◽  
Medical Equipment ◽  
Census Bureau ◽  
Learning Models ◽  
Public Health Emergencies ◽  
Medical Supplies ◽  
Learning Techniques ◽  
Disease Impact ◽  
Random States

Abstract objective This work investigates how reinforcement learning and deep learning models can facilitate the near-optimal redistribution of medical equipment in order to bolster public health responses to future crises similar to the COVID-19 pandemic. materials and methods The system presented is simulated with disease impact statistics from the Institute of Health Metrics (IHME), Center for Disease Control, and Census Bureau[1, 2, 3]. We present a robust pipeline for data preprocessing, future demand inference, and a redistribution algorithm that can be adopted across broad scales and applications. results The reinforcement learning redistribution algorithm demonstrates performance optimality ranging from 93-95%. Performance improves consistently with the number of random states participating in exchange, demonstrating average shortage reductions of 78.74% (± 30.8) in simulations with 5 states to 93.50% (± 0.003) with 50 states. conclusion These findings bolster confidence that reinforcement learning techniques can reliably guide resource allocation for future public health emergencies.

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