Lifelong Latent Actor-Critic (LILAC)

From
Jump to: navigation, search

YouTube search... ...Google search


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. Lifelong Latent Actor-Critic, aka LILAC, uses latent variable models and a maximum entropy policy to leverage past experience for better sample efficiency and performance in dynamic environments. Stanford AI researchers introduce LILAC, reinforcement learning for dynamic environments | Khari Johnson - VentureBeat

Continuous Action