scholarly journals Mice adaptively generate choice variability in a deterministic task

2019 ◽  
Author(s):  
Marwen Belkaid ◽  
Elise Bousseyrol ◽  
Romain Durand-de Cuttoli ◽  
Malou Dongelmans ◽  
Etienne K. Duranté ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Random Selection ◽  
Decision Making Process ◽  
Reward Delivery ◽  
Complex Sequences ◽  
Deterministic Setting

AbstractCan our choices just be driven by chance? To investigate this question, we designed a deterministic setting in which mice reinforce non-repetitive choice sequences, and modeled it using reinforcement learning. Mice progressively increased their choice variability using a memory-free, pseudo-random selection, rather than by learning complex sequences. Our results demonstrate that a decision-making process can self-generate variability and randomness even when the rules governing reward delivery are not stochastic.

scholarly journals Mice adaptively generate choice variability in a deterministic task

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Marwen Belkaid ◽  
Elise Bousseyrol ◽  
Romain Durand-de Cuttoli ◽  
Malou Dongelmans ◽  
Etienne K. Duranté ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Success Rate ◽  
Decision Maker ◽  
Environmental Conditions ◽  
Optimal Strategy ◽  
Temporal Structure ◽  
Decision Making Process ◽  
Reward Delivery ◽  
Deterministic Setting

AbstractCan decisions be made solely by chance? Can variability be intrinsic to the decision-maker or is it inherited from environmental conditions? To investigate these questions, we designed a deterministic setting in which mice are rewarded for non-repetitive choice sequences, and modeled the experiment using reinforcement learning. We found that mice progressively increased their choice variability. Although an optimal strategy based on sequences learning was theoretically possible and would be more rewarding, animals used a pseudo-random selection which ensures high success rate. This was not the case if the animal is exposed to a uniform probabilistic reward delivery. We also show that mice were blind to changes in the temporal structure of reward delivery once they learned to choose at random. Overall, our results demonstrate that a decision-making process can self-generate variability and randomness, even when the rules governing reward delivery are neither stochastic nor volatile.

scholarly journals Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach

2020 ◽  
Vol 34 (04) ◽  
pp. 6210-6218
Author(s):  
Jun Wang ◽  
Hefu Zhang ◽  
Qi Liu ◽  
Zhen Pan ◽  
Hanqing Tao
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Reinforcement Learning ◽  
Decision Making Process ◽  
Learning Approach ◽  
Continuous Control ◽  
Significant Issue ◽  
Fast Growing ◽  
The Relationship ◽  
Slow Growing

Recent years have witnessed the increasing interests in research of crowdfunding mechanism. In this area, dynamics tracking is a significant issue but is still under exploration. Existing studies either fit the fluctuations of time-series or employ regularization terms to constrain learned tendencies. However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics. To address the problem, in this paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm to predict the funding progress in crowdfunding. Specifically, actor-critic frameworks are employed to model the relationship between investors and campaigns, where all of the investors are viewed as an agent that could interact with the environment derived from the real dynamics of campaigns. Then, to further explore the in-depth implications of patterns (i.e., typical characters) in funding series, we propose to subdivide them into fast-growing and slow-growing ones. Moreover, for the purpose of switching from different kinds of patterns, the actor component of TC3 is extended with a structure of options, which comes to the TC3-Options. Finally, extensive experiments on the Indiegogo dataset not only demonstrate the effectiveness of our methods, but also validate our assumption that the entire pattern learned by TC3-Options is indeed the U-shaped one.

Reinforcement learning

2019 ◽  
pp. 206-232
Author(s):  
Thomas P. Trappenberg
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Recent Progress ◽  
Decision Making Process ◽  
Learning Condition ◽  
Learning Problem ◽  
Markov Decision ◽  
Common Learning ◽  
Simple Feedback

The discussion here considers a much more common learning condition where an agent, such as a human or a robot, has to learn to make decisions in the environment from simple feedback. Such feedback is provided only after periods of actions in the form of reward or punishment without detailing which of the actions has contributed to the outcome. This type of learning scenario is called reinforcement learning. This learning problem is formalized in a Markov decision-making process with a variety of related algorithms. The second part of this chapter will use function approximators with neural networks which have made recent progress as deep reinforcement learning.

scholarly journals A reinforcement learning method for decision making process of watermark strength in still images

2011 ◽  
Vol 6 (10) ◽  
pp. 2119-2128 ◽  
Author(s):  
Latif Alimohammad ◽  
Reza Naghsh Nilchi Ahmad ◽  
Derhami Vali
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Decision Making Process ◽  
Learning Method ◽  
Still Images

AAC Decision-Making and Mobile Technology: Points to Ponder

2014 ◽  
Vol 23 (2) ◽  
pp. 104-111 ◽  
Author(s):  
Mary Ann Abbott ◽  
Debby McBride
Keyword(s):  
Decision Making ◽  
Mobile Technology ◽  
Communication System ◽  
Augmentative And Alternative Communication ◽  
Evaluation Process ◽  
Decision Making Process ◽  
Alternative Communication ◽  
Ipod Touch ◽  
Feature Match

The purpose of this article is to outline a decision-making process and highlight which portions of the augmentative and alternative communication (AAC) evaluation process deserve special attention when deciding which features are required for a communication system in order to provide optimal benefit for the user. The clinician then will be able to use a feature-match approach as part of the decision-making process to determine whether mobile technology or a dedicated device is the best choice for communication. The term mobile technology will be used to describe off-the-shelf, commercially available, tablet-style devices like an iPhone®, iPod Touch®, iPad®, and Android® or Windows® tablet.

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