2 papers
cs.LG2026
Policy Gradient for Continuous-Time Robust Markov Decision Processes
Tanya Veeravalli, David M. Bossens, Atsushi Nitanda
The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under worst-case transition dynamic…
cs.LG2026
Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation
Atsushi Nitanda, Dake Bu, Yueming Lyu +1
We study Slowly Annealed Langevin Dynamics (SALD), a sampler for tracking a path of moving target distributions and approximating the terminal target through time slowdown. We esta…