Investors and regulators can greatly benefit from a realistic market
simulator that enables them to anticipate the consequences of their decisions
in real markets. However, traditional rule-based market simulators often fall
short in accurately capturing the dynamic behavior of market participants,
particularly in response to external market impact events or changes in the
behavior of other participants. In this study, we explore an agent-based
simulation framework employing reinforcement learning (RL) agents. We present
the implementation details of these RL agents and demonstrate that the
simulated market exhibits realistic stylized facts observed in real-world
markets. Furthermore, we investigate the behavior of RL agents when confronted
with external market impacts, such as a flash crash. Our findings shed light on
the effectiveness and adaptability of RL-based agents within the simulation,
offering insights into their response to significant market events.

我们研究了一种基于强化学习代理的代理市场模拟框架，并展示了该框架在模拟市场的实现细节和真实市场观察到的特征。我们还研究了强化学习代理在面临外部市场影响（如闪崩）时的行为，并对其在重要市场事件中的响应提供了深入洞察。

智能体市场模拟中的强化学习：揭示现实的艺术事实和行为

Reinforcement Learning in Agent-Based Market Simulation: Unveiling  Realistic Stylized Facts and Behavior

Convolutional Neural Networks (CNNs) have been proven to be extremely
successful at solving computer vision tasks. State-of-the-art methods favor
such deep network architectures for its accuracy performance, with the cost of
having massive number of parameters and high weights redundancy. Previous works
have studied how to prune such CNNs weights. In this paper, we go to another
extreme and analyze the performance of a network stacked with a single
convolution kernel across layers, as well as other weights sharing techniques.
We name it Deep Anchored Convolutional Neural Network (DACNN). Sharing the same
kernel weights across layers allows to reduce the model size tremendously, more
precisely, the network is compressed in memory by a factor of L, where L is the
desired depth of the network, disregarding the fully connected layer for
prediction. The number of parameters in DACNN barely increases as the network
grows deeper, which allows us to build deep DACNNs without any concern about
memory costs. We also introduce a partial shared weights network (DACNN-mix) as
well as an easy-plug-in module, coined regulators, to boost the performance of
our architecture. We validated our idea on 3 datasets: CIFAR-10, CIFAR-100 and
SVHN. Our results show that we can save massive amounts of memory with our
model, while maintaining a high accuracy performance.

本文介绍了一种名为 Deep Anchored Convolutional Neural Network (DACNN) 的卷积神经网络架构，它使用单个卷积核并采用权重共享技术进行网络压缩，同时增加了一个名为 regulators 的易于插入的模块来提高性能，在 CIFAR-10、CIFAR-100 和 SVHN 数据集上验证了其准确性与显著的内存节省。