基于循环强化学习和LSTM神经网络的Agent激励交易
Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks
摘要：
随着计算能力和深度神经网络的突破，许多我们没有用过去严格研究的各种技术探索的领域都是可行的。在本文中，我们将介绍实现机器人交易或建议的可能概念。为了实现类似的性能和通用性水平，就像人类交易员一样，我们的代理人自己学习创造成功的策略，从而获得人类水平的长期奖励。该学习模型在长短期记忆（LSTM）递归结构中实现，强化学习或进化策略作为代理，系统的鲁棒性和可行性在GBPUSD交易中得到了验证。
英文摘要：
With the breakthrough of computational power and deep neural networks, many areas that we haven't explore with various techniques that was researched rigorously in past is feasible. In this paper, we will walk through possible concepts to achieve robo-like trading or advising. In order to accomplish similar level of performance and generality, like a human trader, our agents learn for themselves to create successful strategies that lead to the human-level long-term rewards. The learning model is implemented in Long Short Term Memory (LSTM) recurrent structures with Reinforcement Learning or Evolution Strategies acting as agents The robustness and feasibility of the system is verified on GBPUSD trading.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
