Reinforcement Learning
Sequential decision-making for the real world.
Reinforcement learning (RL) has provided solutions for several difficult sequential decision-making problems in games, locomotion, and robotics. At PFN, we aim to address challenges in reinforcement learning to enable real-world applications. Our research includes deep RL, safety, robotics, model-based RL, and imitation learning.
Publications
Toward Onboard Control System for Mobile Robots via Deep Reinforcement Learning
Deep Reinforcement Learning Workshop at NeurIPS 2018
By : Megumi Miyashita, Shirou Maruyama, Yasuhiro Fujita, Mitsuru Kusumoto, Tobias Pfeiffer, Eiichi Matsumoto, Ryosuke Okuta, Daisuke Okanohara
