scholarly journals The equilibrium of venture capital incentive contract: Optimization and Q-learning approaches

2020 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
seyed Hossein Jafarpour Rezaei ◽  
Mohammad Ali Rastegar
Keyword(s):  
Venture Capital ◽  
Learning Approaches ◽  
Incentive Contract ◽  
Q Learning

scholarly journals Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches

Systems ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 55
Author(s):  
Sarah Bai ◽  
Yijun Zhao
Keyword(s):  
Machine Learning ◽  
Venture Capital ◽  
Balanced Scorecard ◽  
Investment Decisions ◽  
Influential Factors ◽  
Learning Approaches ◽  
Learning Models ◽  
Venture Capital Firm ◽  
Feature Selection Technique ◽  
Machine Learning Models

This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm.

scholarly journals Reinforcement Learning vs Genetic Algorithms in Game-Theoretic Cyber-Security

2018 ◽  
Author(s):  
Stefan Niculae
Keyword(s):  
Reinforcement Learning ◽  
Cyber Security ◽  
Large Scale ◽  
Human Performance ◽  
Learning Approaches ◽  
Classifier Systems ◽  
Penetration Testing ◽  
Q Learning ◽  
Game Theoretic ◽  
Security Game

Penetration testing is the practice of performing a simulated attack on a computer system in order to reveal its vulnerabilities. The most common approach is to gain information and then plan and execute the attack manually, by a security expert. This manual method cannot meet the speed and frequency required for efficient, large-scale secu- rity solutions development. To address this, we formalize penetration testing as a security game between an attacker who tries to compro- mise a network and a defending adversary actively protecting it. We compare multiple algorithms for finding the attacker’s strategy, from fixed-strategy to Reinforcement Learning, namely Q-Learning (QL), Extended Classifier Systems (XCS) and Deep Q-Networks (DQN). The attacker’s strength is measured in terms of speed and stealthi- ness, in the specific environment used in our simulations. The results show that QL surpasses human performance, XCS yields worse than human performance but is more stable, and the slow convergence of DQN keeps it from achieving exceptional performance, in addition, we find that all of these Machine Learning approaches outperform fixed-strategy attackers.

scholarly journals Selective auditory attention detection using dynamic learning systems: The study of RNN and reinforcement learning

2021 ◽  
Author(s):  
Masoud Geravanchizadeh ◽  
Hossein Roushan
Keyword(s):  
Reinforcement Learning ◽  
Detection System ◽  
Auditory Attention ◽  
Final Decision ◽  
Learning Approaches ◽  
Cocktail Party ◽  
Dynamic Learning ◽  
Learning Stage ◽  
Q Learning ◽  
Markov Decision

AbstractThe cocktail party phenomenon describes the ability of the human brain to focus auditory attention on a particular stimulus while ignoring other acoustic events. Selective auditory attention detection (SAAD) is an important issue in the development of brain-computer interface systems and cocktail party processors. This paper proposes a new dynamic attention detection system to process the temporal evolution of the input signal. In the proposed dynamic system, after preprocessing of the input signals, the probabilistic state space of the system is formed. Then, in the learning stage, different dynamic learning methods, including recurrent neural network (RNN) and reinforcement learning (Markov decision process (MDP) and deep Q-learning) are applied to make the final decision as to the attended speech. Among different dynamic learning approaches, the evaluation results show that the deep Q-learning approach (MDP+RNN) provides the highest classification accuracy (94.2%) with the least detection delay. The proposed SAAD system is advantageous, in the sense that the detection of attention is performed dynamically for the sequential inputs. Also, the system has the potential to be used in scenarios, where the attention of the listener might be switched in time in the presence of various acoustic events.

Training Multiagent Systems by Q-Learning: Approaches and Empirical Results

2014 ◽  
Vol 31 (3) ◽  
pp. 498-512 ◽  
Author(s):  
Jose Manuel Lopez-Guede ◽  
Borja Fernandez-Gauna ◽  
Manuel Graña ◽  
Ekaitz Zulueta
Keyword(s):  
Multiagent Systems ◽  
Learning Approaches ◽  
Q Learning ◽  
Empirical Results

Network Parameter Setting for Reinforcement Learning Approaches Using Neural Networks

2011 ◽  
Vol 15 (7) ◽  
pp. 822-930 ◽  
Author(s):  
Kazuaki Yamada ◽  
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Learning Algorithms ◽  
Mapping Function ◽  
Learning Approaches ◽  
Autonomous Mobile Robot ◽  
Q Learning ◽  
Initial Values ◽  
Continuous State

Reinforcement learning approaches are attracting attention as a technique for constructing a trial-anderror mapping function between sensors and motors of an autonomous mobile robot. Conventional reinforcement learning approaches use a look-up table to express the mapping function between grid state and grid action spaces. The grid size greatly adversely affects the learning performance of reinforcement learning algorithms. To avoid this, researchers have proposed reinforcement learning algorithms using neural networks to express the mapping function between continuous state space and action. A designer, however, must set the number of middle neurons and initial values of weight parameters appropriately to improve the approximate accuracy of neural networks. This paper proposes a new method that automatically sets the number ofmiddle neurons and initial values of weight parameters based on the dimension number of the sensor space. The feasibility of proposed method is demonstrated using an autonomous mobile robot navigation problem and is evaluated by comparing it with two types of Q-learning as follows: Q-learning using RBF networks and Q-learning using neural networks whose parameters are set by a designer.

scholarly journals A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2862
Author(s):  
Samuel Yanes Luis ◽  
Daniel Gutiérrez-Reina ◽  
Sergio Toral Marín
Keyword(s):  
Water Quality ◽  
Reinforcement Learning ◽  
Evolutionary Algorithm ◽  
Stability And Convergence ◽  
Learning Approaches ◽  
Q Learning ◽  
Autonomous Surface Vehicles ◽  
High Space ◽  
The Stability ◽  
Water Quality Sensors

The monitoring of water resources using Autonomous Surface Vehicles with water-quality sensors has been a recent approach due to the advances in unmanned transportation technology. The Ypacaraí Lake, the biggest water resource in Paraguay, suffers from a major contamination problem because of cyanobacteria blooms. In order to supervise the blooms using these on-board sensor modules, a Non-Homogeneous Patrolling Problem (a NP-hard problem) must be solved in a feasible amount of time. A dimensionality study is addressed to compare the most common methodologies, Evolutionary Algorithm and Deep Reinforcement Learning, in different map scales and fleet sizes with changes in the environmental conditions. The results determined that Deep Q-Learning overcomes the evolutionary method in terms of sample-efficiency by 50–70% in higher resolutions. Furthermore, it reacts better than the Evolutionary Algorithm in high space-state actions. In contrast, the evolutionary approach shows a better efficiency in lower resolutions and needs fewer parameters to synthesize robust solutions. This study reveals that Deep Q-learning approaches exceed in efficiency for the Non-Homogeneous Patrolling Problem but with many hyper-parameters involved in the stability and convergence.

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