PAC Reinforcement Learning without Real-World Feedback

no code implementations • 23 Sep 2019 • Yuren Zhong, Aniket Anand Deshmukh, Clayton Scott

This work studies reinforcement learning in the Sim-to-Real setting, in which an agent is first trained on a number of simulators before being deployed in the real world, with the aim of decreasing the real-world sample complexity requirement.

reinforcement-learning Reinforcement Learning (RL)