Sparse Cooperative Q-Learning

Total Page:16

File Type:pdf, Size:1020Kb

Sparse Cooperative Q-Learning Sparse Cooperative Q-learning Jelle R. Kok [email protected] Nikos Vlassis [email protected] Informatics Institute, Faculty of Science, University of Amsterdam, The Netherlands Abstract in uncertain environments. In principle, it is pos- sible to treat a multiagent system as a `big' single Learning in multiagent systems suffers from agent and learn the optimal joint policy using stan- the fact that both the state and the action dard single-agent reinforcement learning techniques. space scale exponentially with the number of However, both the state and action space scale ex- agents. In this paper we are interested in ponentially with the number of agents, rendering this using Q-learning to learn the coordinated ac- approach infeasible for most problems. Alternatively, tions of a group of cooperative agents, us- we can let each agent learn its policy independently ing a sparse representation of the joint state- of the other agents, but then the transition model de- action space of the agents. We first examine pends on the policy of the other learning agents, which a compact representation in which the agents may result in oscillatory behavior. need to explicitly coordinate their actions only in a predefined set of states. Next, we On the other hand, in many problems the agents only use a coordination-graph approach in which need to coordinate their actions in few states (e.g., two we represent the Q-values by value rules that cleaning robots that want to clean the same room), specify the coordination dependencies of the while in the rest of the states the agents can act in- agents at particular states. We show how Q- dependently. Even if these `coordinated' states are learning can be efficiently applied to learn a known in advance, it is not a priori clear how the coordinated policy for the agents in the above agents can learn to act cooperatively in these states. framework. We demonstrate the proposed In this paper we describe a multiagent Q-learning tech- method on the predator-prey domain, and we nique, called Sparse Cooperative Q-learning, that al- compare it with other related multiagent Q- lows a group of agents to learn how to jointly solve a learning methods. task when the global coordination requirements of the system (but not the particular action choices of the agents) are known beforehand. 1. Introduction We first examine a compact representation in which A multiagent system (MAS) consists of a group of the agents learn to take joint actions in a predefined agents that can potentially interact with each other set of states. In all other (uncoordinated) states, we (Weiss, 1999; Vlassis, 2003). In this paper, we are let the agents learn independently. Then we generalize interested in fully cooperative multiagent systems in this approach by using a context-specific coordination which the agents have to learn to optimize a global graph (Guestrin et al., 2002b) to specify the coordina- performance measure. One of the key problems in such tion dependencies of subsets of agents according to the systems is the problem of coordination: how to ensure current context (dynamically). The proposed frame- that the individual decisions of the agents result in work allows for a sparse representation of the joint jointly optimal decisions for the group. state-action space of the agents, resulting in large com- putational savings. Reinforcement learning (RL) techniques (Sutton & Barto, 1998) have been applied successfully in many We demonstrate the proposed technique on the single-agent systems for learning the policy of an agent `predator-prey' domain, a popular multiagent problem in which a number of predator agents try to capture st Appearing in Proceedings of the 21 International Confer- a poor prey. Our method achieves a good trade-off ence on Machine Learning, Banff, Canada, 2004. Copyright between speed and solution quality. 2004 by the authors. 2. MDPs and Q-learning and Ri : S × A ! IR is the reward function that re- turns the reward Ri(s; a) for agent i after the joint In this section, we review the Markov Decision Pro- action a is taken in state s. As global reward function cess (MDP) framework. An observable MDP is a tuple n R(s; a) = i=1 Ri(s; a) we take the sum of all individ- hS; A; T; Ri where S is a finite set of world states, A is ual rewardsPreceived by the n agents. This framework a set of actions, T : S × A × S ! [0; 1] is the Marko- differs from a stochastic game (Shapley, 1953) in that vian transition function that describes the probabil- 0 0 each agent wants to maximize social welfare (sum of ity p(s js; a) of ending up in state s when perform- all payoffs) instead of its own payoff. ing action a in state s, and R : S × A ! IR is a reward function that returns the reward R(s; a) ob- Within this framework different choices can be made tained after taking action a in state s. An agent's which affect the problem description and possible solu- policy is defined as a mapping π : S ! A. The tion concepts, e.g., whether the agents are allowed to objective is to find an optimal policy π∗ that maxi- communicate, whether they observe the selected joint mizes the expected discounted future reward U ∗(s) = action, whether they perceive the individual rewards 1 t maxπ E [ t=0 γ R(st)jπ; s0 = s] for each state s. The of the other agents, etc. In our case we assume that the expectationP operator E[·] averages over reward and agents are allowed to communicate and thus are able stochastic transitions and γ 2 [0; 1) is the discount fac- to share individual actions and rewards. Before we dis- tor. We can also represent this using Q-values which cuss our approach, we first describe two other learning store the expected discounted future reward for each methods for environments with multiple agents. state s and possible action a: 3.1. MDP Learners ∗ 0 ∗ 0 0 Q (s; a) = R(s; a)+γ p(s js; a) max Q (s ; a ): (1) X a0 s0 In principle, a collaborative multiagent MDP can be regarded as one large single agent in which each joint The optimal policy for a state s is the action ∗ action is represented as a single action. The optimal arg maxa Q (s; a) that maximizes the expected future Q-values for the joint actions can then be learned us- discounted reward. ing standard single-agent Q-learning. In order to apply Reinforcement learning (RL) (Sutton & Barto, 1998) this MDP learners approach a central controller mod- can be applied to estimate Q∗(s; a). Q-learning is a els the complete MDP and communicates to each agent widely used learning method when the transition and its individual action, or all agents model the complete reward model are unavailable. This method starts MDP separately and select the individual action that with an initial estimate Q(s; a) for each state-action corresponds to their own identity. In the latter case, no pair. When an exploration action a is taken in state s, communication is needed between the agents but they reward R(s; a) is received and next state s0 is observed, all have to observe the joint action and all individual the corresponding Q-value is updated by rewards. Moreover, the problem of exploration can be solved by using the same random number generator 0 0 Q(s; a) := Q(s; a)+α[R(s; a)+γ max Q(s ; a )−Q(s; a)] (and the same seed) for all agents (Vlassis, 2003). Al- a0 (2) though this approach leads to the optimal solution, it where α 2 (0; 1) is an appropriate learning rate. Un- is infeasible for problems with many agents since the der conditions, Q-learning is known to converge to the joint action space, which is exponential in the number optimal Q∗(s; a) (Watkins & Dayan, 1992). of agents, becomes intractable. 3. Multiagent Q-learning 3.2. Independent Learners At the other extreme, we have the independent learn- The framework discussed in the previous section only ers (IL) approach (Claus & Boutilier, 1998) in which involves single agents. In this work, we are interested the agents ignore the actions and rewards of the other in systems in which multiple agents, each with their agents in the system, and learn their strategies in- own set of actions, have to collaboratively solve a task. dependently. The standard convergence proof for Q- A collaborative multiagent MDP (Guestrin, 2003) ex- learning does not hold in this case, since the transition tends the single agent MDP framework to include mul- model depends on the unknown policy of the other tiple agents whose joint action impacts the state tran- learning agents. Despite the lack of guaranteed con- sition and the received reward. Now, the transition vergence, this method has been applied successfully in model T : S × A × S ! [0; 1] represents the proba- multiple cases (Tan, 1993; Sen et al., 1994). bility p(s0js; a) the system will move from state s to 0 n s after performing the joint action a 2 A = ×i=1Ai PSfrag replacements s s0 s00 4. Context-Specific Q-learning 0 R1(s; a) 0 R1(s ; a) Q1(s ; a1) In many problems, agents only have to coordinate their A1 actions in a specific context (Guestrin et al., 2002b). 0 For example, two cleaning robots only have to take R2(s; a) 0 R2(s ; a) 00 A2 Q(s; a) Q2(s ; a2) Q(s ; a) care that they do not obstruct each other when they are cleaning the same room.
Recommended publications
  • Backpropagation and Deep Learning in the Brain
    Backpropagation and Deep Learning in the Brain Simons Institute -- Computational Theories of the Brain 2018 Timothy Lillicrap DeepMind, UCL With: Sergey Bartunov, Adam Santoro, Jordan Guerguiev, Blake Richards, Luke Marris, Daniel Cownden, Colin Akerman, Douglas Tweed, Geoffrey Hinton The “credit assignment” problem The solution in artificial networks: backprop Credit assignment by backprop works well in practice and shows up in virtually all of the state-of-the-art supervised, unsupervised, and reinforcement learning algorithms. Why Isn’t Backprop “Biologically Plausible”? Why Isn’t Backprop “Biologically Plausible”? Neuroscience Evidence for Backprop in the Brain? A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: How to convince a neuroscientist that the cortex is learning via [something like] backprop - To convince a machine learning researcher, an appeal to variance in gradient estimates might be enough. - But this is rarely enough to convince a neuroscientist. - So what lines of argument help? How to convince a neuroscientist that the cortex is learning via [something like] backprop - What do I mean by “something like backprop”?: - That learning is achieved across multiple layers by sending information from neurons closer to the output back to “earlier” layers to help compute their synaptic updates. How to convince a neuroscientist that the cortex is learning via [something like] backprop 1. Feedback connections in cortex are ubiquitous and modify the
    [Show full text]
  • Implementing Machine Learning & Neural Network Chip
    Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP Implementing Machine Learning & Neural Network Chip Architectures USING NETWORK-ON-CHIP INTERCONNECT IP TY GARIBAY Chief Technology Officer [email protected] Title: Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP Primary author: Ty Garibay, Chief Technology Officer, ArterisIP, [email protected] Secondary author: Kurt Shuler, Vice President, Marketing, ArterisIP, [email protected] Abstract: A short tutorial and overview of machine learning and neural network chip architectures, with emphasis on how network-on-chip interconnect IP implements these architectures. Time to read: 10 minutes Time to present: 30 minutes Copyright © 2017 Arteris www.arteris.com 1 Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP What is Machine Learning? “ Field of study that gives computers the ability to learn without being explicitly programmed.” Arthur Samuel, IBM, 1959 • Machine learning is a subset of Artificial Intelligence Copyright © 2017 Arteris 2 • Machine learning is a subset of artificial intelligence, and explicitly relies upon experiential learning rather than programming to make decisions. • The advantage to machine learning for tasks like automated driving or speech translation is that complex tasks like these are nearly impossible to explicitly program using rule-based if…then…else statements because of the large solution space. • However, the “answers” given by a machine learning algorithm have a probability associated with them and can be non-deterministic (meaning you can get different “answers” given the same inputs during different runs) • Neural networks have become the most common way to implement machine learning.
    [Show full text]
  • Predrnn: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal Lstms
    PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang Mingsheng Long∗ School of Software School of Software Tsinghua University Tsinghua University [email protected] [email protected] Jianmin Wang Zhifeng Gao Philip S. Yu School of Software School of Software School of Software Tsinghua University Tsinghua University Tsinghua University [email protected] [email protected] [email protected] Abstract The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal vari- ations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial ap- pearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures. 1 Introduction
    [Show full text]
  • Introduction to Machine Learning
    Introduction to Machine Learning Perceptron Barnabás Póczos Contents History of Artificial Neural Networks Definitions: Perceptron, Multi-Layer Perceptron Perceptron algorithm 2 Short History of Artificial Neural Networks 3 Short History Progression (1943-1960) • First mathematical model of neurons ▪ Pitts & McCulloch (1943) • Beginning of artificial neural networks • Perceptron, Rosenblatt (1958) ▪ A single neuron for classification ▪ Perceptron learning rule ▪ Perceptron convergence theorem Degression (1960-1980) • Perceptron can’t even learn the XOR function • We don’t know how to train MLP • 1963 Backpropagation… but not much attention… Bryson, A.E.; W.F. Denham; S.E. Dreyfus. Optimal programming problems with inequality constraints. I: Necessary conditions for extremal solutions. AIAA J. 1, 11 (1963) 2544-2550 4 Short History Progression (1980-) • 1986 Backpropagation reinvented: ▪ Rumelhart, Hinton, Williams: Learning representations by back-propagating errors. Nature, 323, 533—536, 1986 • Successful applications: ▪ Character recognition, autonomous cars,… • Open questions: Overfitting? Network structure? Neuron number? Layer number? Bad local minimum points? When to stop training? • Hopfield nets (1982), Boltzmann machines,… 5 Short History Degression (1993-) • SVM: Vapnik and his co-workers developed the Support Vector Machine (1993). It is a shallow architecture. • SVM and Graphical models almost kill the ANN research. • Training deeper networks consistently yields poor results. • Exception: deep convolutional neural networks, Yann LeCun 1998. (discriminative model) 6 Short History Progression (2006-) Deep Belief Networks (DBN) • Hinton, G. E, Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554. • Generative graphical model • Based on restrictive Boltzmann machines • Can be trained efficiently Deep Autoencoder based networks Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H.
    [Show full text]
  • Survey on Reinforcement Learning for Language Processing
    Survey on reinforcement learning for language processing V´ıctorUc-Cetina1, Nicol´asNavarro-Guerrero2, Anabel Martin-Gonzalez1, Cornelius Weber3, Stefan Wermter3 1 Universidad Aut´onomade Yucat´an- fuccetina, [email protected] 2 Aarhus University - [email protected] 3 Universit¨atHamburg - fweber, [email protected] February 2021 Abstract In recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language process- ing tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. This paper reviews the state of the art of RL methods for their possible use for different problems of natural language processing, focusing primarily on conversational systems, mainly due to their growing relevance. We provide detailed descriptions of the problems as well as discussions of why RL is well-suited to solve them. Also, we analyze the advantages and limitations of these methods. Finally, we elaborate on promising research directions in natural language processing that might benefit from reinforcement learning. Keywords| reinforcement learning, natural language processing, conversational systems, pars- ing, translation, text generation 1 Introduction Machine learning algorithms have been very successful to solve problems in the natural language pro- arXiv:2104.05565v1 [cs.CL] 12 Apr 2021 cessing (NLP) domain for many years, especially supervised and unsupervised methods. However, this is not the case with reinforcement learning (RL), which is somewhat surprising since in other domains, reinforcement learning methods have experienced an increased level of success with some impressive results, for instance in board games such as AlphaGo Zero [106].
    [Show full text]
  • Lecture 11 Recurrent Neural Networks I CMSC 35246: Deep Learning
    Lecture 11 Recurrent Neural Networks I CMSC 35246: Deep Learning Shubhendu Trivedi & Risi Kondor University of Chicago May 01, 2017 Lecture 11 Recurrent Neural Networks I CMSC 35246 Introduction Sequence Learning with Neural Networks Lecture 11 Recurrent Neural Networks I CMSC 35246 Some Sequence Tasks Figure credit: Andrej Karpathy Lecture 11 Recurrent Neural Networks I CMSC 35246 MLPs only accept an input of fixed dimensionality and map it to an output of fixed dimensionality Great e.g.: Inputs - Images, Output - Categories Bad e.g.: Inputs - Text in one language, Output - Text in another language MLPs treat every example independently. How is this problematic? Need to re-learn the rules of language from scratch each time Another example: Classify events after a fixed number of frames in a movie Need to resuse knowledge about the previous events to help in classifying the current. Problems with MLPs for Sequence Tasks The "API" is too limited. Lecture 11 Recurrent Neural Networks I CMSC 35246 Great e.g.: Inputs - Images, Output - Categories Bad e.g.: Inputs - Text in one language, Output - Text in another language MLPs treat every example independently. How is this problematic? Need to re-learn the rules of language from scratch each time Another example: Classify events after a fixed number of frames in a movie Need to resuse knowledge about the previous events to help in classifying the current. Problems with MLPs for Sequence Tasks The "API" is too limited. MLPs only accept an input of fixed dimensionality and map it to an output of fixed dimensionality Lecture 11 Recurrent Neural Networks I CMSC 35246 Bad e.g.: Inputs - Text in one language, Output - Text in another language MLPs treat every example independently.
    [Show full text]
  • Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting
    water Article Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting Halit Apaydin 1 , Hajar Feizi 2 , Mohammad Taghi Sattari 1,2,* , Muslume Sevba Colak 1 , Shahaboddin Shamshirband 3,4,* and Kwok-Wing Chau 5 1 Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkey; [email protected] (H.A.); [email protected] (M.S.C.) 2 Department of Water Engineering, Agriculture Faculty, University of Tabriz, Tabriz 51666, Iran; [email protected] 3 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam 4 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam 5 Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China; [email protected] * Correspondence: [email protected] or [email protected] (M.T.S.); [email protected] (S.S.) Received: 1 April 2020; Accepted: 21 May 2020; Published: 24 May 2020 Abstract: Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language.
    [Show full text]
  • Deep Learning in Bioinformatics
    Deep Learning in Bioinformatics Seonwoo Min1, Byunghan Lee1, and Sungroh Yoon1,2* 1Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea 2Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea Abstract In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e., deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. *Corresponding author. Mailing address: 301-908, Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea. E-mail: [email protected]. Phone: +82-2-880-1401. Keywords Deep learning, neural network, machine learning, bioinformatics, omics, biomedical imaging, biomedical signal processing Key Points As a great deal of biomedical data have been accumulated, various machine algorithms are now being widely applied in bioinformatics to extract knowledge from big data.
    [Show full text]
  • A Deep Reinforcement Learning Neural Network Folding Proteins
    DeepFoldit - A Deep Reinforcement Learning Neural Network Folding Proteins Dimitra Panou1, Martin Reczko2 1University of Athens, Department of Informatics and Telecommunications 2Biomedical Sciences Research Center “Alexander Fleming” ABSTRACT Despite considerable progress, ab initio protein structure prediction remains suboptimal. A crowdsourcing approach is the online puzzle video game Foldit [1], that provided several useful results that matched or even outperformed algorithmically computed solutions [2]. Using Foldit, the WeFold [3] crowd had several successful participations in the Critical Assessment of Techniques for Protein Structure Prediction. Based on the recent Foldit standalone version [4], we trained a deep reinforcement neural network called DeepFoldit to improve the score assigned to an unfolded protein, using the Q-learning method [5] with experience replay. This paper is focused on model improvement through hyperparameter tuning. We examined various implementations by examining different model architectures and changing hyperparameter values to improve the accuracy of the model. The new model’s hyper-parameters also improved its ability to generalize. Initial results, from the latest implementation, show that given a set of small unfolded training proteins, DeepFoldit learns action sequences that improve the score both on the training set and on novel test proteins. Our approach combines the intuitive user interface of Foldit with the efficiency of deep reinforcement learning. KEYWORDS: ab initio protein structure prediction, Reinforcement Learning, Deep Learning, Convolution Neural Networks, Q-learning 1. ALGORITHMIC BACKGROUND Machine learning (ML) is the study of algorithms and statistical models used by computer systems to accomplish a given task without using explicit guidelines, relying on inferences derived from patterns. ML is a field of artificial intelligence.
    [Show full text]
  • An Introduction to Deep Reinforcement Learning
    An Introduction to Deep Reinforcement Learning Ehsan Abbasnejad Remember: Supervised Learning We have a set of sample observations, with labels learn to predict the labels, given a new sample cat Learn the function that associates a picture of a dog/cat with the label dog Remember: supervised learning We need thousands of samples Samples have to be provided by experts There are applications where • We can’t provide expert samples • Expert examples are not what we mimic • There is an interaction with the world Deep Reinforcement Learning AlphaGo Scenario of Reinforcement Learning Observation Action State Change the environment Agent Don’t do that Reward Environment Agent learns to take actions maximizing expected Scenario of Reinforcement Learningreward. Observation Action State Change the environment Agent Thank you. Reward https://yoast.com/how-t Environment o-clean-site-structure/ Machine Learning Actor/Policy ≈ Looking for a Function Action = π( Observation ) Observation Action Function Function input output Used to pick the Reward best function Environment Reinforcement Learning in a nutshell RL is a general-purpose framework for decision-making • RL is for an agent with the capacity to act • Each action influences the agent’s future state • Success is measured by a scalar reward signal Goal: select actions to maximise future reward Deep Learning in a nutshell DL is a general-purpose framework for representation learning • Given an objective • Learning representation that is required to achieve objective • Directly from raw inputs
    [Show full text]
  • Modelling Working Memory Using Deep Recurrent Reinforcement Learning
    Modelling Working Memory using Deep Recurrent Reinforcement Learning Pravish Sainath1;2;3 Pierre Bellec1;3 Guillaume Lajoie 2;3 1Centre de Recherche, Institut Universitaire de Gériatrie de Montréal 2Mila - Quebec AI Institute 3Université de Montréal Abstract 1 In cognitive systems, the role of a working memory is crucial for visual reasoning 2 and decision making. Tremendous progress has been made in understanding the 3 mechanisms of the human/animal working memory, as well as in formulating 4 different frameworks of artificial neural networks. In the case of humans, the [1] 5 visual working memory (VWM) task is a standard one in which the subjects are 6 presented with a sequence of images, each of which needs to be identified as to 7 whether it was already seen or not. Our work is a study of multiple ways to learn a 8 working memory model using recurrent neural networks that learn to remember 9 input images across timesteps in supervised and reinforcement learning settings. 10 The reinforcement learning setting is inspired by the popular view in Neuroscience 11 that the working memory in the prefrontal cortex is modulated by a dopaminergic 12 mechanism. We consider the VWM task as an environment that rewards the 13 agent when it remembers past information and penalizes it for forgetting. We 14 quantitatively estimate the performance of these models on sequences of images [2] 15 from a standard image dataset (CIFAR-100 ) and their ability to remember 16 and recall. Based on our analysis, we establish that a gated recurrent neural 17 network model with long short-term memory units trained using reinforcement 18 learning is powerful and more efficient in temporally consolidating the input spatial 19 information.
    [Show full text]
  • Machine Learning and Data Mining Machine Learning Algorithms Enable Discovery of Important “Regularities” in Large Data Sets
    General Domains Machine Learning and Data Mining Machine learning algorithms enable discovery of important “regularities” in large data sets. Over the past Tom M. Mitchell decade, many organizations have begun to routinely cap- ture huge volumes of historical data describing their operations, products, and customers. At the same time, scientists and engineers in many fields have been captur- ing increasingly complex experimental data sets, such as gigabytes of functional mag- netic resonance imaging (MRI) data describing brain activity in humans. The field of data mining addresses the question of how best to use this historical data to discover general regularities and improve PHILIP NORTHOVER/PNORTHOV.FUTURE.EASYSPACE.COM/INDEX.HTML the process of making decisions. COMMUNICATIONS OF THE ACM November 1999/Vol. 42, No. 11 31 he increasing interest in Figure 1. Data mining application. A historical set of 9,714 medical data mining, or the use of records describes pregnant women over time. The top portion is a historical data to discover typical patient record (“?” indicates the feature value is unknown). regularities and improve The task for the algorithm is to discover rules that predict which T future patients will be at high risk of requiring an emergency future decisions, follows from the confluence of several recent trends: C-section delivery. The bottom portion shows one of many rules the falling cost of large data storage discovered from this data. Whereas 7% of all pregnant women in devices and the increasing ease of the data set received emergency C-sections, the rule identifies a collecting data over networks; the subclass at 60% at risk for needing C-sections.
    [Show full text]