Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects

@inproceedings{Belcak2020FastAS,
  title={Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects},
  author={Peter Belcak and Jan-Peter Calliess and Stefan Zohren},
  booktitle={MABS},
  year={2020}
}
. We introduce a new software toolbox for agent-based simulation. Facilitating rapid prototyping by offering a user-friendly Python API, its core rests on an efficient C++ implementation to support simulation of large-scale multi-agent systems. Our software environment benefits from a versatile message-driven architecture. Originally developed to support research on financial markets, it offers the flexibility to simulate a wide-range of different (easily customisable) market rules and to study the e… 

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