Flow Matching Policy Optimization with Mirror Descent and Entropy Constraints
FMER optimizes reinforcement learning policies using an ODE-based flow matching approach combined with policy mirror descent and explicit entropy constraints. It achieves simulation-free training by regressing a vector field toward an optimal transport velocity, while deriving a closed-form entropy expression that correctly handles bounded action spaces. This allows for principled exploration and multimodal behavior in complex tasks.