Modelling Emotion Based Reward Valuation with Computational Reinforcement Learning

We show that computational reinforcement learning can model human decision making in the Iowa Gambling Task (IGT). The IGT is a card game, which tests decision making under uncertainty. In our experiments, we found that modulating learning rate decay in Q-learning, enables the approximation of both the behaviour of normal subjects and those who are emotionally impaired by ventromedial prefrontal lesions. Outcomes observed in impaired subjects are modeled by high learning rate decay, while low learning rate decay replicates healthy subjects under otherwise identical conditions. The ventromedial prefrontal cortex has been associated with emotion based reward valuation, and, the value function in reinforcement learning provides an analogous assessment mechanism. Thus reinforcement learning can provide a good model for the role of emotional reward as a modulator of the learning rate

A reinforcement learning based decision support system in textile manufacturing process

This paper introduced a reinforcement learning based decision support system in textile manufacturing process. A solution optimization problem of color fading ozonation is discussed and set up as a Markov Decision Process (MDP) in terms of tuple {S, A, P, R}. Q-learning is used to train an agent in the interaction with the setup environment by accumulating the reward R. According to the application result, it is found that the proposed MDP model has well expressed the optimization problem of textile manufacturing process discussed in this paper, therefore the use of reinforcement learning to support decision making in this sector is conducted and proven that is applicable with promising prospects

Transcranial Direct Corrent stimulation (tDCS) of the anterior prefrontal cortex (aPFC) modulates reinforcement learning and decision-making under uncertainty: A doubleblind crossover study

Reinforcement learning refers to the ability to acquire information from the outcomes of prior choices (i.e. positive and negative) in order to make predictions on the effect of future decision and adapt the behaviour basing on past experiences. The anterior prefrontal cortex (aPFC) is considered to play a key role in the representation of event value, reinforcement learning and decision-making. However, a causal evidence of the involvement of this area in these processes has not been provided yet. The aim of the study was to test the role of the orbitofrontal cortex in feedback processing, reinforcement learning and decision-making under uncertainly. Eighteen healthy individuals underwent three sessions of tDCS over the prefrontal pole (anodal, cathodal, sham) during a probabilistic learning (PL) task. In the PL task, participants were invited to learn the covert probabilistic stimulusoutcome association from positive and negative feedbacks in order to choose the best option. Afterwards, a probabilistic selection (PS) task was delivered to assess decisions based on the stimulus-reward associations acquired in the PL task. During cathodal tDCS, accuracy in the PL task was reduced and participants were less prone to maintain their choice after positive feedback or to change it after a negative one (i.e., winstay and lose-shift behavior). In addition, anodal tDCS affected the subsequent PS task by reducing the ability to choose the best alternative during hard probabilistic decisions. In conclusion, the present study suggests a causal role of aPFC in feedback trial-by-trial behavioral adaptation and decision-making under uncertainty

Reinforcement Learning: A Survey

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file