Evolutionary DRL Environment: Transfer Learning-Based Genetic Algorithm

J. Data Intell. Pub Date : 2022-08-01 DOI:10.26421/jdi3.3-3
Badr Hirchoua, Imadeddine Mountasser, B. Ouhbi, B. Frikh
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引用次数: 0

Abstract

Stock markets trading has risen as a critical challenge for artificial intelligence research. The way stock markets are moving and changing pushes researchers to find more sophisticated algorithms and strategies to anticipate the market movement and changes. From the artificial intelligence perspective, such environments require artificial agents to coordinate and transfer their best experience through different generations of agents. However, the existing agents are trained using hand-crafted expert features and expert capabilities. Notwithstanding these refinements, no previous single system has come near to dominating the trading environment. We address the algorithmic trading problem utilising an evolutive learning method. Precisely, we train a multi-agent reinforcement learning algorithm that uses only self trades generated by different generations of agents. The evolution-based genetic algorithm operates as an evolutive environment that continually adapts the agent's internal strategies and tactics. Also, it pushes the system forward to generate creative behaviours for the next generations. Additionally, a deep recurrent neural network drives the mutation mechanism through the attention that dynamically encodes the memory mutation size. The winner, which is the last agent, achieved promising performances and surpassed traditional and intelligent baselines.
进化DRL环境:基于迁移学习的遗传算法
股票市场交易已成为人工智能研究面临的一个关键挑战。股票市场移动和变化的方式促使研究人员寻找更复杂的算法和策略来预测市场的移动和变化。从人工智能的角度来看,这样的环境需要人工智能通过不同代的智能体来协调和传递他们的最佳经验。然而,现有的代理是使用手工制作的专家特征和专家功能来训练的。尽管有这些改进,以前还没有一个单一的系统能接近支配交易环境。我们利用一种进化学习方法来解决算法交易问题。准确地说,我们训练了一个多智能体强化学习算法,该算法只使用由不同代智能体生成的自交易。基于进化的遗传算法作为一个不断适应智能体内部策略和战术的进化环境来运行。此外,它还推动系统向前发展,为下一代产生创造性行为。此外,深层递归神经网络通过动态编码记忆突变大小的注意力来驱动突变机制。最后胜出的agent取得了很好的成绩,超越了传统和智能的基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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