Difference between revisions of "Lifelong Latent Actor-Critic (LILAC)"
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*** Lifelong Latent Actor-Critic (LILAC) | *** Lifelong Latent Actor-Critic (LILAC) | ||
** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| − | + | * [[Policy]] ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]] | |
Researchers from [https://ai.stanford.edu/ 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. [https://venturebeat.com/2020/07/01/stanford-ai-researchers-introduce-lilac-reinforcement-learning-for-dynamic-environments/ Stanford AI researchers introduce LILAC, reinforcement learning for dynamic environments | Khari Johnson - VentureBeat] | Researchers from [https://ai.stanford.edu/ 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. [https://venturebeat.com/2020/07/01/stanford-ai-researchers-introduce-lilac-reinforcement-learning-for-dynamic-environments/ Stanford AI researchers introduce LILAC, reinforcement learning for dynamic environments | Khari Johnson - VentureBeat] | ||
Revision as of 15:40, 16 April 2023
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- Lifelong Learning
- Reinforcement Learning (RL)
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- State-Action-Reward-State-Action (SARSA)
- Q Learning
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- Asynchronous Advantage Actor Critic (A3C)
- Advanced Actor Critic (A2C)
- Lifelong Latent Actor-Critic (LILAC)
- Hierarchical Reinforcement Learning (HRL)
- Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)
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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