Takayuki Osa

Dynamically learning support bounds for value functions can enhance stability and performance in reinforcement learning, outperforming traditional fixed-interval approaches.

Taming policy diversity with KL constraints unlocks surprisingly stable and sample-efficient ensemble reinforcement learning in high-dimensional manipulation tasks.