scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1640 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Yaser Faghan ◽  
Pedram Ghamisi ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
High Dimensional ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Yaser Faghan ◽  
Pedram Ghamisi ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
High Dimensional ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Shahab Shamshirband
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Business Environments ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this work, we first consider a brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Skill-based curiosity for intrinsically motivated reinforcement learning

2019 ◽  
Vol 109 (3) ◽  
pp. 493-512 ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Skill Learning ◽  
High Dimensional ◽  
Sequential Decision ◽  
Learning Methods ◽  
Reward Function ◽  
Intrinsic Reward ◽  
Reinforcement Learning Models ◽  
Data Efficiency

Abstract Reinforcement learning methods rely on rewards provided by the environment that are extrinsic to the agent. However, many real-world scenarios involve sparse or delayed rewards. In such cases, the agent can develop its own intrinsic reward function called curiosity to enable the agent to explore its environment in the quest of new skills. We propose a novel end-to-end curiosity mechanism for deep reinforcement learning methods, that allows an agent to gradually acquire new skills. Our method scales to high-dimensional problems, avoids the need of directly predicting the future, and, can perform in sequential decision scenarios. We formulate the curiosity as the ability of the agent to predict its own knowledge about the task. We base the prediction on the idea of skill learning to incentivize the discovery of new skills, and guide exploration towards promising solutions. To further improve data efficiency and generalization of the agent, we propose to learn a latent representation of the skills. We present a variety of sparse reward tasks in MiniGrid, MuJoCo, and Atari games. We compare the performance of an augmented agent that uses our curiosity reward to state-of-the-art learners. Experimental evaluation exhibits higher performance compared to reinforcement learning models that only learn by maximizing extrinsic rewards.

scholarly journals A deep learning model for gas storage optimization

2021 ◽  
Author(s):  
Nicolas Curin ◽  
Michael Kettler ◽  
Xi Kleisinger-Yu ◽  
Vlatka Komaric ◽  
Thomas Krabichler ◽  
...  
Keyword(s):  
Risk Management ◽  
Monte Carlo ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
State Of The Art ◽  
Gas Storage ◽  
High Dimensional ◽  
Forward Market ◽  
Storage Optimization ◽  
Deep Learning Model

AbstractTo the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. In this article, we utilize techniques inspired by reinforcement learning in order to optimize the operation plans of underground natural gas storage facilities. We provide a theoretical framework and assess the performance of the proposed method numerically in comparison to a state-of-the-art least-squares Monte-Carlo approach. Due to the inherent intricacy originating from the high-dimensional forward market as well as the numerous constraints and frictions, the optimization exercise can hardly be tackled by means of traditional techniques.

scholarly journals Risk-Aware Model-Based Control

2021 ◽  
Vol 8 ◽  
Author(s):  
Chen Yu ◽  
Andre Rosendo
Keyword(s):  
Reinforcement Learning ◽  
Value At Risk ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Training Data ◽  
Conditional Value At Risk ◽  
High Dimensional ◽  
Model Based Control ◽  
Model Based ◽  
Model Free

Model-Based Reinforcement Learning (MBRL) algorithms have been shown to have an advantage on data-efficiency, but often overshadowed by state-of-the-art model-free methods in performance, especially when facing high-dimensional and complex problems. In this work, a novel MBRL method is proposed, called Risk-Aware Model-Based Control (RAMCO). It combines uncertainty-aware deep dynamics models and the risk assessment technique Conditional Value at Risk (CVaR). This mechanism is appropriate for real-world application since it takes epistemic risk into consideration. In addition, we use a model-free solver to produce warm-up training data, and this setting improves the performance in low-dimensional environments and covers the shortage of MBRL’s nature in the high-dimensional scenarios. In comparison with other state-of-the-art reinforcement learning algorithms, we show that it produces superior results on a walking robot model. We also evaluate the method with an Eidos environment, which is a novel experimental method with multi-dimensional randomly initialized deep neural networks to measure the performance of any reinforcement learning algorithm, and the advantages of RAMCO are highlighted.

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