Reinforcement learning based on market simulation at JPMorgan

JP Morgan AI Research is using reinforcement learning (RL) to model complex economic systems and efficient policy learning, which helps them simulate multiple heterogeneous interacting agents in a realistic market environment. The team needed an environment that could simulate the interactions of shops and consumers with diverse preferences, constraints, and connectivity. RL can help learn agent behaviors and policies and calibrate the agent composition with real-world data, addressing challenges such as finding equilibrium among strategic agents and model calibration. By using RL-based simulations built with Ray and RLlib, the team has greatly increased the efficacy and efficiency of their models.

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