Lifelong Latent Actor-Critic (LILAC)

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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

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