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Multi-agent reinforcement learning approach for hedging portfolio problem
2021
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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Inspired by recently achievement of deep reinforcement learning, we explore feasibility to construct a hedging strategy automatically by leveraging cooperative multi-agent in reinforcement learning techniques ...
, tax, and settlement date of transactions) for reinforcement learning agent training. ...
Multi-Agent Reinforcement Learning Learned deep RL agent was deployed to trade out-of-sample market data from May 17, 2019, to May 21, 2020. ...
doi:10.1007/s00500-021-05801-6
pmid:33897298
pmcid:PMC8054257
fatcat:4yse63ba2jcb7e7k5ra432knn4
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking
[article]
2019
arXiv
pre-print
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In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for ...
This paper investigates the counterfactual thinking for agents to find optimal decision-making strategies in multi-agent reinforcement learning environments. ...
We test CFT on standard multi-agent deep reinforcement learning platforms and real-world problems. ...
arXiv:1908.04573v2
fatcat:ouxrpzum7fc7dpnw4mxjm2sfpu
Deep Reinforcement Learning Attention Selection for Person Re-Identification
[article]
2018
arXiv
pre-print
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In this work, we develop a joint learning deep model that optimises person re-id attention selection within any auto-detected person bounding boxes by reinforcement learning of background clutter minimisation ...
Specifically, we formulate a novel unified re-id architecture called Identity DiscriminativE Attention reinforcement Learning (IDEAL) to accurately select re-id attention in auto-detected bounding boxes ...
Re-ID Attention Selection by Reinforcement Learning The Identity DiscriminativE Attention reinforcement Learning (IDEAL) model has two subnetworks: (I) A multi-class discrimination network D by deep learning ...
arXiv:1707.02785v4
fatcat:3fjqw3uopfbjbaq4d4rf2ebyiu
Deep Reinforcement Learning in Agent Based Financial Market Simulation
2020
Journal of Risk and Financial Management
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In this study we propose a framework for training deep reinforcement learning models in agent based artificial price-order-book simulations that yield non-trivial policies under diverse conditions with ...
to find investment strategies using deep reinforcement learning. ...
Proposal and verification of a deep reinforcement learning framework that learns meaningful trading strategies in agent based artificial market simulations 2. ...
doi:10.3390/jrfm13040071
fatcat:6rpzflkjxramvntlhv6py5tuc4
A parallel-network continuous quantitative trading model with GARCH and PPO
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2105.03625v1 fatcat:45jl5ucrnngb3agrclauurpi7y
Experiments in 5 stocks from Chinese stock market show our method achieves more extra profit comparing with basical reinforcement learning methods and bench models. ...
Thus, we propose a parallel-network continuous quantitative trading model with GARCH and PPO to enrich the basical deep reinforcement learning model, where the deep learning parallel network layers deal ...
To tackle these challenges, we propose a multi-frequency continuousshare quantitative trading algorithm with GARCH (MCTG) based on deep reinforcement learning. ...
arXiv:2105.03625v2
fatcat:rof7n76si5ajjm77zqybeocyrm
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2112.06753v1 fatcat:ud4cnswqjza5xcmyd3pfhcu5s4
Deep reinforcement learning (DRL) has shown huge potentials in building financial market simulators recently. ...
In this paper, we present a FinRL-Meta framework that builds a universe of market environments for data-driven financial reinforcement learning. ...
Compared to traditional simulation models, deep reinforcement learning (DRL) has shown huge potentials in building financial market simulators through multi-agent systems [10] . ...
arXiv:2112.06753v2
fatcat:mdrhekejcngirlsxeq67mfushq
Optimizing Market Making using Multi-Agent Reinforcement Learning
[article]
2018
arXiv
pre-print
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In this paper, reinforcement learning is applied to the problem of optimizing market making. ...
A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The framework consists of two agents. ...
Q-Learning The specific reinforcement learning algorithm used for both agents is deep Q-learning. ...
arXiv:1812.10252v1
fatcat:bbisnxloyjdglecu5mbcpxcnny
Deep Hierarchical Strategy Model For Multi-Source Driven Quantitative Investment
2019
IEEE Access
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Using deep learning to maximize the benefits of a series of risks in the securities market was a very interesting and widely concerned problem. ...
deep deterministic policy gradient (DDPG), which is one of the reinforcement learning algorithms to decide continuous trading position. ...
INTRODUCTION In recent years, supervised learning and reinforcement learning have been applied to the research of stock quantitative investment [1] - [4] . ...
doi:10.1109/access.2019.2923267
fatcat:bur5qnonenfitg2kuad7ehsucm
TFCMA-DRL: Tolerant Flexible Coordinated Multi-Agent Deep Reinforcement Learning for Prediction of Future Stock Price Trends from Multi-Source Data
2021
International Journal of Intelligent Engineering and Systems
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To tackle these issues, the Tolerant Flexible Coordinated Multi-Agent Deep Reinforcement Learning (TFCMA-DRL) model is proposed in this paper. ...
Machine learning and deep learning algorithms were the most efficient techniques used for prediction by extracting public opinions and events. ...
In this paper, Coordination policy is integrated with Multi-Agent Deep Reinforcement Learning to form TFCMA-DRL based stock price forecasting model for multi-source data. ...
doi:10.22266/ijies2021.0430.04
fatcat:xrsrb7yjeneorgvsbzlw5tb63y
Deep reinforcement learning for portfolio management
[article]
2022
arXiv
pre-print
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In our paper, we apply deep reinforcement learning approach to optimize investment decisions in portfolio management. ...
The experimental results show that our model is able to optimize investment decisions and has the ability to obtain excess return in stock market, and the optimized agent maintains the asset weights at ...
Reinforcement learning has four basic elements: agent, state or environment, action and reward. The agent takes actions to obtain rewards in the state and gets to the next state. ...
arXiv:2012.13773v7
fatcat:vbgiqqg6lbfyphyd44ve7h7ymu
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics
[article]
2020
arXiv
pre-print
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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. ...
Although, there were some recent research that applied multi-agent reinforcement learning (MARL) scenarios to a small set of agents but not to a large set of agents [126] . ...
arXiv:2004.01509v1
fatcat:4ggjzkfdi5fe3g7uwb7dwbvtue
Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms
2021
Electronics
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This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing ...
When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high ...
The proposed coordinated multi-agent deep reinforcement learning (DRL) (MADRL)based autonomous and intelligent energy sharing in order to minimize energy purchases from the local energy market for minimizing ...
doi:10.3390/electronics10050543
fatcat:jhjkpioqgveafg4fafjuy6iefq
Evolutionary DRL Environment: Transfer Learning-Based Genetic Algorithm
2022
Journal of Data Intelligence
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Precisely, we train a multi-agent reinforcement learning algorithm that uses only self trades generated by different generations of agents. ...
We address the algorithmic trading problem utilising an evolutive learning method. ...
deep reinforcement learning, transfer learning, and multi-agent learning. ...
doi:10.26421/jdi3.3-3
fatcat:xls454ztibeinow2tnbpxk4ysy
Correlated Deep Q-learning based Microgrid Energy Management
2020
2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
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2021 pre-print arXiv:2103.04152v1 fatcat:lnulw3sixndppmx3ngqo2chtiu
In this paper, the Long Short Term Memory networks (LSTM) based deep Q-learning algorithm is introduced and the correlated equilibrium is proposed to coordinate agents. ...
Considering the significant potential of machine learning techniques, this paper proposes a correlated deep Q-learning (CDQN) based technique for the MG energy management. ...
In MARL, each agent is expected to learn the cooperation strategy. Naturally, MARL can also be generalized to multi-agent deep reinforcement learning (MADRL). ...
doi:10.1109/camad50429.2020.9209254
dblp:conf/camad/ZhouE20
fatcat:zebjmiwh7bdn7alf3irgvvzo5y
Fairness in Multi-agent Reinforcement Learning for Stock Trading
[article]
2019
arXiv
pre-print
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In this paper, we propose a novel scheme that utilizes multi-agent reinforcement learning systems to derive stock trading strategies for all clients which keep a balance between revenue and fairness. ...
First, we demonstrate that Reinforcement learning (RL) is able to learn from experience and adapt the trading strategies to the complex market environment. ...
Multi-agent Reinforcement Learning Setting The advantage of multi-agent over single-agent reinforcement learning is the ability to incorporate high-level complexities in the system. ...
arXiv:2001.00918v1
fatcat:4sb7z3n3h5hr5hzubgcwghtxyq
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