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model-based reinforcement learning

Yun Lin, Chao Wang, Jiaxing Wang, Zheng Dou
Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks...
October 12, 2016: Sensors
Jason M Scimeca, Perri L Katzman, David Badre
Adaptive memory requires context-dependent control over how information is retrieved, evaluated and used to guide action, yet the signals that drive adjustments to memory decisions remain unknown. Here we show that prediction errors (PEs) coded by the striatum support control over memory decisions. Human participants completed a recognition memory test that incorporated biased feedback to influence participants' recognition criterion. Using model-based fMRI, we find that PEs-the deviation between the outcome and expected value of a memory decision-correlate with striatal activity and predict individuals' final criterion...
October 7, 2016: Nature Communications
Ruoqing Zhu, Ying-Qi Zhao, Guanhua Chen, Shuangge Ma, Hongyu Zhao
We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree algorithm to pursue signals in a high-dimensional space. In the first step, we transform the treatment selection problem into a weighted classification problem that can utilize tree-based methods. In the second step, we adopt a newly proposed tree-based method, known as reinforcement learning trees, to detect features involved in the optimal treatment rules and to construct binary splitting rules...
October 4, 2016: Biometrics
John P O'Doherty, Jeffrey Cockburn, Wolfgang M Pauli
In this review, we summarize findings supporting the existence of multiple behavioral strategies for controlling reward-related behavior, including a dichotomy between the goal-directed or model-based system and the habitual or model-free system in the domain of instrumental conditioning and a similar dichotomy in the realm of Pavlovian conditioning. We evaluate evidence from neuroscience supporting the existence of at least partly distinct neuronal substrates contributing to the key computations necessary for the function of these different control systems...
September 28, 2016: Annual Review of Psychology
Michael A Perelman
The Sexual Tipping Point(®) (STP) model is an integrated approach to the etiology, diagnosis and treatment of men with delayed ejaculation (DE), including all subtypes manifesting ejaculatory delay or absence [registered trademark owned by the MAP Educational Fund, a 501(c)(3) public charity]. A single pathogenetic pathway does not exist for sexual disorders generally and that is also true for DE specifically. Men with DE have various bio-psychosocial-behavioral & cultural predisposing, precipitating, maintaining, and contextual factors which trigger, reinforce, or worsen the probability of DE occurring...
August 2016: Translational Andrology and Urology
BingKan Xue, Stanislas Leibler
Organisms can adapt to a randomly varying environment by creating phenotypic diversity in their population, a phenomenon often referred to as "bet hedging." The favorable level of phenotypic diversity depends on the statistics of environmental variations over timescales of many generations. Could organisms gather such long-term environmental information to adjust their phenotypic diversity? We show that this process can be achieved through a simple and general learning mechanism based on a transgenerational feedback: The phenotype of the parent is progressively reinforced in the distribution of phenotypes among the offspring...
October 4, 2016: Proceedings of the National Academy of Sciences of the United States of America
Stefan Elfwing, Eiji Uchibe, Kenji Doya
Free-energy based reinforcement learning (FERL) was proposed for learning in high-dimensional state and action spaces. However, the FERL method does only really work well with binary, or close to binary, state input, where the number of active states is fewer than the number of non-active states. In the FERL method, the value function is approximated by the negative free energy of a restricted Boltzmann machine (RBM). In our earlier study, we demonstrated that the performance and the robustness of the FERL method can be improved by scaling the free energy by a constant that is related to the size of network...
August 26, 2016: Neural Networks: the Official Journal of the International Neural Network Society
Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derives the optimal policy using the transition model. However, learning an accurate transition model in high-dimensional environments requires a large amount of data which is difficult to obtain. To overcome this difficulty, in this paper, we propose to combine model-based reinforcement learning with the recently developed least-squares conditional entropy (LSCE) method, which simultaneously performs transition model estimation and dimension reduction...
August 24, 2016: Neural Networks: the Official Journal of the International Neural Network Society
Rebecca Boehme, Robert C Lorenz, Tobias Gleich, Lydia Romund, Patricia Pelz, Sabrina Golde, Eva Flemming, Andrew Wold, Lorenz Deserno, Joachim Behr, Diana Raufelder, Andreas Heinz, Anne Beck
Adolescence is a critical maturation period for human cognitive control and executive function. In this study, a large sample of adolescents (n = 85) performed a reversal learning task during functional magnetic resonance imaging. We analyzed behavioral data using a reinforcement learning model to provide individually fitted parameters and imaging data with regard to reward prediction errors (PE). Following a model-based approach, we formed two groups depending on whether individuals tended to update expectations predominantly for the chosen stimulus or also for the unchosen one...
September 15, 2016: European Journal of Neuroscience
Yuzhe Li, Ken Nakae, Shin Ishii, Honda Naoki
Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC)...
September 2016: PLoS Computational Biology
Robert Lowe, Alexander Almér, Gustaf Lindblad, Pierre Gander, John Michael, Cordula Vesper
Joint Action is typically described as social interaction that requires coordination among two or more co-actors in order to achieve a common goal. In this article, we put forward a hypothesis for the existence of a neural-computational mechanism of affective valuation that may be critically exploited in Joint Action. Such a mechanism would serve to facilitate coordination between co-actors permitting a reduction of required information. Our hypothesized affective mechanism provides a value function based implementation of Associative Two-Process (ATP) theory that entails the classification of external stimuli according to outcome expectancies...
2016: Frontiers in Computational Neuroscience
(no author information available yet)
School readiness includes not only the early academic skills of children but also their physical health, language skills, social and emotional development, motivation to learn, creativity, and general knowledge. Families and communities play a critical role in ensuring children's growth in all of these areas and thus their readiness for school. Schools must be prepared to teach all children when they reach the age of school entry, regardless of their degree of readiness. Research on early brain development emphasizes the effects of early experiences, relationships, and emotions on creating and reinforcing the neural connections that are the basis for learning...
September 2016: Pediatrics
Wouter Kool, Fiery A Cushman, Samuel J Gershman
Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding...
August 2016: PLoS Computational Biology
Ronald Thom, Tom St Clair, Rebecca Burns, Michael Anderson
Adaptive management (AM) is being employed in a number of programs in the United States to guide actions to restore aquatic ecosystems because these programs are both expensive and are faced with significant uncertainties. Many of these uncertainties are associated with prioritizing when, where, and what kind of actions are needed to meet the objectives of enhancing ecosystem services and recovering threatened and endangered species. We interviewed nine large-scale aquatic ecosystem restoration programs across the United States to document the lessons learned from implementing AM...
December 1, 2016: Journal of Environmental Management
Alan S R Fermin, Takehiko Yoshida, Junichiro Yoshimoto, Makoto Ito, Saori C Tanaka, Kenji Doya
Humans can select actions by learning, planning, or retrieving motor memories. Reinforcement Learning (RL) associates these processes with three major classes of strategies for action selection: exploratory RL learns state-action values by exploration, model-based RL uses internal models to simulate future states reached by hypothetical actions, and motor-memory RL selects past successful state-action mapping. In order to investigate the neural substrates that implement these strategies, we conducted a functional magnetic resonance imaging (fMRI) experiment while humans performed a sequential action selection task under conditions that promoted the use of a specific RL strategy...
2016: Scientific Reports
Joshua J Tremel, Patryk A Laurent, David A Wolk, Mark E Wheeler, Julie A Fiez
Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task...
December 15, 2016: Behavioural Brain Research
Arkady Konovalov, Ian Krajbich
Organisms appear to learn and make decisions using different strategies known as model-free and model-based learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Here using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset...
2016: Nature Communications
Belén Rubio Ballester, Martina Maier, Rosa María San Segundo Mozo, Victoria Castañeda, Armin Duff, Paul F M J Verschure
BACKGROUND: After stroke, patients who suffer from hemiparesis tend to suppress the use of the affected extremity, a condition called learned non-use. Consequently, the lack of training may lead to the progressive deterioration of motor function. Although Constraint-Induced Movement Therapies (CIMT) have shown to be effective in treating this condition, the method presents several limitations, and the high intensity of its protocols severely compromises its adherence. We propose a novel rehabilitation approach called Reinforcement-Induced Movement Therapy (RIMT), which proposes to restore motor function through maximizing arm use...
2016: Journal of Neuroengineering and Rehabilitation
Désirée A Lie, Christopher P Forest, Anne Walsh, Yvonne Banzali, Kevin Lohenry
BACKGROUND: The student-run clinic (SRC) has the potential to address interprofessional learning among health professions students. PURPOSE: To derive a framework for understanding student learning during team-based care provided in an interprofessional SRC serving underserved patients. METHODS: The authors recruited students for a focus group study by purposive sampling and snowballing. They constructed two sets of semi-structured questions for uniprofessional and multiprofessional groups...
2016: Medical Education Online
W Van Lippevelde, J Vangeel, N De Cock, C Lachat, L Goossens, K Beullens, L Vervoort, C Braet, L Maes, S Eggermont, B Deforche, J Van Camp
BACKGROUND: As the snacking pattern of European adolescents is of great concern, effective interventions are necessary. Till now health promotion efforts in children and adolescents have had only limited success in changing adolescents' eating patterns and anthropometrics. Therefore, the present study proposes an innovative approach to influence dietary behaviors in youth based on new insights on effective behavior change strategies and attractive intervention channels to engage adolescents...
2016: BMC Public Health
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