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.
By : Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel
Deep Reinforcement Learning Workshop at NeurIPS 2018