Stanford AI researchers introduce LILAC, reinforcement learning for dynamic environments

Venture Beat | Jul 1, 2020 at 7:08 PM
  • Researchers from Stanford AI Lab (SAIL) have devised a method to deal with data and environments that change over time in a way that outperforms some leading approaches to reinforcement learning.
  • Authors conducted four tests in dynamic reinforcement learning environments including a Sawyer robot from the Meta-World benchmark, a Half-Cheetah in OpenAI Gym, and a 2D navigation task.
  • Lead author Xie also worked with UC Berkeley professor Sergey Levine on SAC and SLAC.