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
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning
ECML 2021
By : Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
ChainerRL: A Deep Reinforcement Learning Library
JMLR 2021
By : Yasuhiro Fujita, Prabhat Nagarajan, KataokaToshiki, Takahiro Ishikawa
MANGA: Method Agnostic Neural-policy Generalization and Adaptation
ICRA 2020
By : Homanga Bharadhwaj, Shoichiro Yamaguchi, Shin-ichi Maeda
Behaviorally Diverse Traffic Simulation via Reinforcement Learning
IROS 2020
By : Shinya Shiroshita, Shirou Maruyama, Daisuke Nishiyama, Mario Castro, Karim Hamzaoui, Guy Rosman, Jonathan DeCastro, Kuan-Hui Lee, Adrien Gaidon
