scholarly journals A Computational View on the Nature of Reward and Value in Anhedonia

2021 ◽  
Author(s):  
Michael Browning ◽  
Quentin JM Huys
Keyword(s):  
Reinforcement Learning ◽  
Subjective Experience ◽  
Treatment Resistance ◽  
Trade Off ◽  
Belief Structure ◽  
Model Based ◽  
Recent Advances ◽  
Novel Approaches ◽  
Descriptive Framework

Anhedonia—a common feature of depression—encompasses a reduction in the subjective experience and anticipation of rewarding events, and a reduction in the motivation to seek out such events. The presence of anhedonia often predicts or accompanies treatment resistance, and as such better interventions and treatments are important. Yet the mechanisms giving rise to anhedonia are not well-understood. In this chapter, we briefly review existing computational conceptualisations of anhedonia. We argue that they are mostly descriptive and fail to provide anexplanatory account of why anhedonia may occur. Working within the framework of reinforcement learning, we examine two potential computational mechanisms that could give rise to anhedonic phenomena. First, we show how anhedonia can arise in multidimensional drive reduction settings through a trade-off between different rewards or needs. We then generalize this in terms of model-based value inference and identify a key role for associational belief structure. We close with a brief discussion of treatment implications of both of these conceptualisations. Insummary, computational accounts of anhedonia have provided a useful descriptive framework. Recent advances in reinforcement learning suggest promising avenues by which the mechanisms underlying anhedonia may be teased apart, potentially motivating novel approaches to treatment.

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

Recent Advances in Reinforcement Learning for Traffic Signal Control

2021 ◽  
Vol 22 (2) ◽  
pp. 12-18 ◽  
Author(s):  
Hua Wei ◽  
Guanjie Zheng ◽  
Vikash Gayah ◽  
Zhenhui Li
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Advantages And Disadvantages ◽  
Recent Advances

Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems

Bestärkendes Lernen mittels Offline-Trajektorienplanung basierend auf iterativ approximierten Modellen

2020 ◽  
Vol 68 (8) ◽  
pp. 612-624
Author(s):  
Max Pritzkoleit ◽  
Robert Heedt ◽  
Carsten Knoll ◽  
Klaus Röbenack
Keyword(s):  
Reinforcement Learning ◽  
Neuronale Netze ◽  
Model Based ◽  
Künstliche Neuronale Netze

ZusammenfassungIn diesem Beitrag nutzen wir Künstliche Neuronale Netze (KNN) zur Approximation der Dynamik nichtlinearer (mechanischer) Systeme. Diese iterativ approximierten neuronalen Systemmodelle werden in einer Offline-Trajektorienplanung verwendet, um eine optimale Rückführung zu bestimmen, welche auf das reale System angewandt wird. Dieser Ansatz des modellbasierten bestärkenden Lernens (engl. model-based reinforcement learning (RL)) wird am Aufschwingen des Einfachwagenpendels zunächst simulativ evaluiert und zeigt gegenüber modellfreien RL-Ansätzen eine signifikante Verbesserung der Dateneffizienz. Weiterhin zeigen wir Experimentalergebnisse an einem Versuchsstand, wobei der vorgestellte Algorithmus innerhalb weniger Versuche in der Lage ist, eine für das System optimale Rückführung hinreichend gut zu approximieren.

scholarly journals Uncertainty-aware Contact-safe Model-based Reinforcement Learning

2021 ◽  
pp. 1-1
Author(s):  
Cheng-Yu Kuo ◽  
Andreas Schaarschmidt ◽  
Yunduan Cui ◽  
Tamim Asfour ◽  
Takamitsu Matsubara
Keyword(s):  
Reinforcement Learning ◽  
Model Based ◽  
Safe Model

A Model-Based Factored Bayesian Reinforcement Learning Approach

2014 ◽  
Vol 513-517 ◽  
pp. 1092-1095
Author(s):  
Bo Wu ◽  
Yan Peng Feng ◽  
Hong Yan Zheng
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Iteration Algorithm ◽  
Value Iteration ◽  
Practical Applications ◽  
Model Based ◽  
Online Planning ◽  
Bayesian Reinforcement Learning ◽  
Bayesian Inference Method ◽  
Unknown Structure

Bayesian reinforcement learning has turned out to be an effective solution to the optimal tradeoff between exploration and exploitation. However, in practical applications, the learning parameters with exponential growth are the main impediment for online planning and learning. To overcome this problem, we bring factored representations, model-based learning, and Bayesian reinforcement learning together in a new approach. Firstly, we exploit a factored representation to describe the states to reduce the size of learning parameters, and adopt Bayesian inference method to learn the unknown structure and parameters simultaneously. Then, we use an online point-based value iteration algorithm to plan and learn. The experimental results show that the proposed approach is an effective way for improving the learning efficiency in large-scale state spaces.

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