The proposed framework addresses the intertwined challenges of trading liquidity and funding stability for supply chain finance Real World Assets (RWAs) by positioning liquidity provision as an active, risk-aware decision problem. An Automated Market Maker (AMM), paired with deposits, provides continuous liquidity for RWA holders, while a funding vault supplies reserves and helps absorb financial shocks. Liquidity providers must dynamically manage their inventory risk as stochastic trading flows alter the balance between RWAs and deposits, requiring adaptive funding rates to attract capital and maintain solvency buffers. As multiple RWAs with heterogeneous credit qualities and correlated risks are pooled, asset pricing becomes jointly driven by credit fundamentals, market demand, and funding conditions, significantly increasing system complexity.

This high-dimensional, stochastic environment motivates a risk-sensitive Markov decision process (MDP) framework that captures both terminal-stage reward risk and long-run process risk using measures such as variance, CVaR, VaR, and the Sharpe ratio. Traditional dynamic programming techniques are insufficient due to non-additive and non-Markovian risk metrics, prompting the use of advanced approaches including constrained risk-sensitive MDPs, reinforcement learning, and continuous-time reinforcement learning (CTRL). The research advances policy optimization, convergence analysis, and regret bounds, while extending from single-agent to multi-agent settings to model strategic interactions between liquidity providers and funding agents in transparent, competitive blockchain environments. A particular contribution is the novel treatment of Sharpe ratio optimization and solvency-constrained decision-making within MDPs.

Building on these theoretical foundations, the project will deliver an Integrated RWA Liquidity Engine that combines risk-sensitive optimization with multi-agent reinforcement learning to jointly manage AMM spreads and funding vault solvency. Over the project period, the engine will be validated through sandbox-style simulations that reflect realistic tokenized-deposit liquidity conditions, including inventory shocks, correlated credit events, and funding stress. Performance will be assessed using institutionally meaningful metrics such as reserve-buffer breach probabilities, recovery time after shocks, stability of funding rates, and liquidity depth. The resulting system will provide a governed, auditable, and production-ready solution for sustainable liquidity and funding management across tokenized private credit and broader RWA markets.