Difference between revisions of "Lifelong Latent Actor-Critic (LILAC)"
| Line 9: | Line 9: | ||
* [[Lifelong Learning]] | * [[Lifelong Learning]] | ||
| − | * [[Reinforcement Learning (RL)]] | + | * [[Reinforcement Learning (RL)]] |
** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ||
** [[Markov Decision Process (MDP)]] | ** [[Markov Decision Process (MDP)]] | ||
| + | ** [[State-Action-Reward-State-Action (SARSA)]] | ||
** [[Q Learning]] | ** [[Q Learning]] | ||
| − | ** [[ | + | *** [[Deep Q Network (DQN)]] |
** [[Deep Reinforcement Learning (DRL)]] DeepRL | ** [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
** [[Distributed Deep Reinforcement Learning (DDRL)]] | ** [[Distributed Deep Reinforcement Learning (DDRL)]] | ||
| − | |||
** [[Evolutionary Computation / Genetic Algorithms]] | ** [[Evolutionary Computation / Genetic Algorithms]] | ||
** [[Actor Critic]] | ** [[Actor Critic]] | ||
| + | *** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
*** [[Advanced Actor Critic (A2C)]] | *** [[Advanced Actor Critic (A2C)]] | ||
| − | |||
*** Lifelong Latent Actor-Critic (LILAC) | *** Lifelong Latent Actor-Critic (LILAC) | ||
** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| + | |||
Researchers from [http://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. [http://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 [http://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. [http://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 07:16, 6 July 2020
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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)
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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