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Deep Reinforcement Learning in Agent Based Financial Market Simulation

机译：基于Agent的金融市场模拟中的深增强学习

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Iwao Maeda; David deGraw; Michiharu Kitano; Hiroyasu Matsushima; Hiroki Sakaji; Kiyoshi Izumi; Atsuo Kato;
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1. Deep Reinforcement Learning in Agent Based Financial Market Simulation [J] . Maeda Iwao, deGraw David, Kitano Michiharu, Journal of Risk and Financial Management . 2020,第4期

机译：基于Agent的金融市场模拟中的深增强学习
2. Deep reinforcement learning based trading agents: Risk curiosity driven learning for financial rules-based policy [J] . Hirchoua Badr, Ouhbi Brahim, Frikh Bouchra Expert systems with applications . 2021,第May期

机译：基于深度强化学习的交易代理：金融规则的危险效力驱动学习
3. Approximating Nash Equilibrium in Day-ahead Electricity Market Bidding with Multi-agent Deep Reinforcement Learning [J] . Yan Du, Fangxing Li, Helia Zandi, 现代电力系统与清洁能源学报(英文) . 2021,第003期
4. Trading Switching Setup Based on Reinforcement Learning Applied to a Multiagent System Simulation of Financial Markets [C] . Michel C. R. Leles, Elton F. Sbruzzi, de Oliveira José M. P., Annual IEEE International Systems Conference . 2019

机译：基于强化学习的交易切换设置在金融市场多主体系统仿真中的应用
5. Macro-Action-Based Multi-Agent Deep Reinforcement Learning in Cooperative Tasks [D] . Lu, Xingyu. 2021

机译：基于宏观动作的多智能经济型深度加强学习合作任务
6. Colloquium PaperAdaptive Agents Intelligence and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling: Short-memory traders and their impact on group learning in financial markets [O] . Blake LeBaron 2002

机译：座谈会论文自适应代理情报和新兴人类组织：通过基于代理的建模来捕捉复杂性：短期交易者及其对金融市场中群体学习的影响
7. Approximating Nash Equilibrium in Day-ahead Electricity Market Bidding with Multi-agent Deep Reinforcement Learning [O] . Yan Du, Fangxing Li, Helia Zandi, 2021

机译：用多师深度加固学习近期电力市场竞标的纳什均衡

Deep Reinforcement Learning in Agent Based Financial Market Simulation

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