FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance

2025-09-22

AI Summary

Traditional stochastic control methods in finance suffer from poor robustness in dynamic real-world markets due to strong assumptions—e.g., market stationarity and Markovianity. To address this, we propose an adaptive decision-making framework integrating imitation learning with noise-augmented latent-space reinforcement learning. First, a meta-policy is pre-trained via multi-expert imitation learning; then, it is fine-tuned via reinforcement learning in a noisy latent space. An action chunking mechanism is further introduced to explicitly model non-Markovian dependencies. This design enables cross-market knowledge transfer and online policy adaptation. Empirical results demonstrate that our method significantly outperforms single-expert baselines across diverse market regimes—including high-volatility and structural-break scenarios—while exhibiting superior generalization, stability, and real-time decision robustness.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Multiagent Learning

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Traditional stochastic control methods in finance rely on simplifying assumptions that often fail in real world markets. While these methods work well in specific, well defined scenarios, they underperform when market conditions change. We introduce FinFlowRL, a novel framework for financial stochastic control that combines imitation learning with reinforcement learning. The framework first pretrains an adaptive meta policy by learning from multiple expert strategies, then finetunes it through reinforcement learning in the noise space to optimize the generation process. By employing action chunking, that is generating sequences of actions rather than single decisions, it addresses the non Markovian nature of financial markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions.

Problem

• Addresses limitations of traditional financial stochastic control methods
• Combines imitation and reinforcement learning for adaptive market control
• Handles non-Markovian market dynamics through action chunking techniques

Innovation

• Combines imitation learning with reinforcement learning
• Pretrains meta policy from multiple expert strategies
• Uses action chunking for non-Markovian market adaptation