By training models based on reward signals (essentially rewarding them for correct reasoning steps or final answers) RL has ... reasoning. Algorithms such as Proximal Policy Optimization ...

These models, while powerful, often stumble when it comes to consistency or tackling complex, multi-step problems. That’s where reinforcement learning (RL) comes in—a way to refine and guide ...

Proximal Policy Optimization, Twin Delayed Deep Deterministic Policy Gradient, and Soft Actor-Critic are consistently shown to be the top-performing model-free actor-critic algorithms used for robotic ...

Abstract: As a key step in the IC design flow, logic synthesis ... consisting of these logic optimization algorithms based on their knowledge. To overcome this limitation, in this brief, reinforcement ...