Vela: Nonlinear Solutions to DSGE Models via Multi-Agent Reinforcement Learning in JAX

Dynamic stochastic general equilibrium (DSGE) models are widely used by macroeconomic institutions for policy-making and forecasting. However, traditional approaches often rely on log-linearization around a pre-specified steady state, yielding linear approximations whose validity is limited to its neighborhood. We present Vela, a JAX-based framework for defining and solving DSGE models through parallel multi-agent reinforcement learning. We implement the Kydland–Prescott RBC and Smets–Wouters DSGE models in Vela. Using OECD data for ten G20 economies from 1960 to 2026, we estimate empirical impulse responses through vector autoregression (VAR) and compare their directions with model predictions. Solutions learned via multi-agent self-play achieve higher accuracy than a conventionally solved Smets–Wouters baseline in eight of the ten G20 economies studied, with statistically significant improvements. The learned policies capture nonlinear dynamics, including interactions between simultaneous shocks, and can be meta-trained across a continuous range of economic calibration parameters. We also examine the design choices that affect learned policy quality and compare the resulting solutions with conventional DSGE solution methods and empirical data.