Lifelong Latent Actor-Critic (LILAC)

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• Reinforcement Learning (RL):
  • Monte Carlo (MC) Method - Model Free Reinforcement Learning
  • Markov Decision Process (MDP)
  • Q Learning
  • State-Action-Reward-State-Action (SARSA)
  • Deep Reinforcement Learning (DRL) DeepRL
  • Distributed Deep Reinforcement Learning (DDRL)
  • Deep Q Network (DQN)
  • Evolutionary Computation / Genetic Algorithms
  • Actor Critic
    • Advanced Actor Critic (A2C)
    • Asynchronous Advantage Actor Critic (A3C)
    • Lifelong Latent Actor-Critic (LILAC)
  • Hierarchical Reinforcement Learning (HRL)

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