Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients

arXiv:2605. 14297v1 Announce Type: cross Abstract: We study reinforcement learning in hybrid discrete-continuous action spaces, such as settings where the discrete component selects a regime (or index) and the continuous component optimizes within it -- a structure common in robotics, control, and operations problems.