Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations, Novermber 2022#
2. Affiliation:#
J.P. Morgan AI Research
3. Keywords:#
Multi-Agent Reinforcement Learning, Over-The-Counter market, parameterized families, reward functions, shared
policy learning, emergent behaviors, game equilibrium.
4. Urls:#
Paper: https://arxiv.org/abs/2210.07184v1
Github: None
5. Summary:#
(1): The article presents an approach to multi-agent reinforcement learning for efficient and effective market simulations in Over-The-Counter (OTC) markets such as the foreign exchange market.
(2): Prior methods fail to learn emergent behaviors of the liquidity provider and liquidity taker agents in OTC markets. The article proposes a suitable design of parameterized families of reward functions coupled with associated shared policy learning, enabling agents to learn emergent behaviors relative to a wide spectrum of incentives encompassing profit-and-loss, optimal execution, and market share. The approach is well motivated, emphasizing the difficulty of modeling OTC markets and the need for a more efficient and effective solution.
(3): The paper proposes a supertype-based multi-agent simulation model, where agent behaviors are learned via reinforcement learning using a shared policy conditioned on agent type. The paper also introduces a deep-reinforcement-learning-driven approach to design OTC agents that balances hedging and skewing based on agents’ incentives, which are connected to inventory. Moreover, the paper details a novel RL-based calibration algorithm that enforces game equilibrium, which performed well on both toy and real market data.
(4): The proposed approach achieves the best performance on various evaluation metrics, such as profit-and-loss, market share, and optimal execution, relative to the available prior approaches. The approach supports the goals of providing efficient and effective market simulations in OTC markets.
6. Conclusion:#
(1): The significance of this work lies in proposing a new approach to Multi-Agent Reinforcement Learning specifically for Over-The-Counter (OTC) markets, which have been difficult to model efficiently and effectively in the past. The proposed approach achieves the best performance on various evaluation metrics and supports the efficient and effective simulation of OTC markets.
(2): Innovation point: The paper proposes a supertype-based multi-agent simulation model with designed parameterized families of reward functions and associated shared policy learning, creating agents that learn emergent behaviors relative to a wide spectrum of incentives. It also introduces a deep-reinforcement-learning-driven approach to designing OTC agents that balances hedging and skewing based on agents’ incentives connected to inventory.
(3): Performance: The proposed approach achieves the best performance on various evaluation metrics, such as profit-and-loss, market share, and optimal execution, relative to the available prior approaches.
(4): Workload: The paper provides a clear and detailed explanation of the proposed approach, but the technical nature of the content may require substantial background knowledge for readers unfamiliar with the field.