Researchers introduced a noisy-space action-value (Q-)function and a noisy-space policy gradient (NSPG) that trains diffusion policies for offline reinforcement learning without backpropagating throug…
Researchers propose CoDrift, a compositional framework for offline reinforcement learning that combines three objective-level fields into a unified policy field, achieving the best average rank across…
Researchers introduce CODS, a critic-guided data selection method for reusable offline reinforcement learning, which at a 10% budget retains 96.6% of eligible-pool performance across 20 D4RL task-algo…
Researchers led by Tonio Ball introduced the Inverter framework, a neuro-inspired approach for embodied planning and control that uses paired forward/inverse internal models and open-loop multi-step m…