论文标题
通过加强学习（SAC）进行营销
Market-making with reinforcement-learning (SAC)
论文作者
论文摘要
该论文探讨了连续的动作空间软演奏者批评（SAC）增强学习模型在自动市场制定领域的应用。强化学习代理人接收了模拟的客户交易流，从而在资产中获得了职位，并学会通过模拟的“交换”价差或通过更改客户提供的客户差价来抵消这种风险（偏向于提供的价格）。学习最低利差的问题弥补了接受该职位的风险的问题。最后，代理商的问题是学会对冲由独立价格流程（“投资组合”职位）产生的混合客户贸易流的问题。引入了位置惩罚方法以改善融合。引入了与开放式健身房兼容的对冲环境，并将开放的AI SAC基线RL引擎用作学习基线。
The paper explores the application of a continuous action space soft actor-critic (SAC) reinforcement learning model to the area of automated market-making. The reinforcement learning agent receives a simulated flow of client trades, thus accruing a position in an asset, and learns to offset this risk by either hedging at simulated "exchange" spreads or by attracting an offsetting client flow by changing offered client spreads (skewing the offered prices). The question of learning minimum spreads that compensate for the risk of taking the position is being investigated. Finally, the agent is posed with a problem of learning to hedge a blended client trade flow resulting from independent price processes (a "portfolio" position). The position penalty method is introduced to improve the convergence. An Open-AI gym-compatible hedge environment is introduced and the Open AI SAC baseline RL engine is being used as a learning baseline.
