深度强化学习的计算性能 学习找到纳什平衡
Christoph Graf1,2, Viktor Zobernig3, Johannes Schmidt3
1Institute for Policy Integrity, New York University, New York, NY 10012 USA.
概括
深度强化学习算法可以在价格竞争拍卖中找到纳什平衡. 特定参数调整实现了高达99%的伯特兰平衡趋同,在复杂的市场模拟中被证明是有效的.
科学领域:
- 计算经济学计算经济学
- 人工智能的人工智能
- 游戏理论 游戏理论
背景情况:
- 在统一价格拍卖会上以价格竞争的公司寻求纳什平衡.
- 深度强化学习 (DRL) 提供了分析复杂市场动态的潜力.
- 传统的DRL通常是"无模型",依赖于广泛的参数调.
研究的目的:
- 为了评估深度决定性政策梯度 (DDPG) 在价格竞争中找到纳什平衡的性能.
- 系统地分析DDPG参数配置对伯特兰平衡趋同的影响.
- 评估DDPG在更复杂的多人拍卖设置中的适用性.
主要方法:
- 使用深度决定性政策梯度 (DDPG),用于连续状态和动作空间的DRL算法.
- 系统地改变了DRL算法参数 (学习速度,内存缓冲区等). ) 的情况.
- 在统一价格拍卖模型中,与分析衍生的伯特朗平衡相比较的趋同.
主要成果:
- 识别了DDPG参数配置,实现高达99%的融合率.
- 在具有多个参与者和各种成本结构的环境中证明了可靠的融合.
- 验证了DDPG超越简单市场模式的有效性.
结论:
- 优化的 DDPG 参数设置使得在价格竞争中高保真性对纳什平衡的收成为可能.
- DRL,特别是DDPG,是研究复杂的拍卖环境中的战略公司行为的一个强有力的工具.
- 这种方法有助于在复杂的市场模拟中分析经济策略.
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