Difference between revisions of "Hierarchical Reinforcement Learning (HRL)"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
}} | }} | ||
| − | [ | + | [https://www.youtube.com/results?search_query=Hierarchical+Reinforcement+Learning Youtube search...] |
| − | [ | + | [https://www.google.com/search?q=Hierarchical+Reinforcement+machine+learning+ML+artificial+intelligence ...Google search] |
| − | * [ | + | * [https://thegradient.pub/the-promise-of-hierarchical-reinforcement-learning The Promise of Hierarchical Reinforcement Learning | Yannis Flet-Berliac - The Gradient] |
| − | + | * [https://www.slideshare.net/DavidJardim/hierarchical-reinforcement-learning Hierarchical Reinforcement Learning | David Jardim] | |
| − | * [ | + | |
| − | * [[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]] | ||
| − | ** [[Asynchronous Advantage Actor Critic (A3C)]] | + | ** [[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)]] | ||
| + | |||
| + | HRL is a promising approach to extend traditional [[Reinforcement Learning (RL)]] methods to solve more complex tasks. | ||
<youtube>x_QjJry0hTc</youtube> | <youtube>x_QjJry0hTc</youtube> | ||
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<youtube>zQy02LsARo0</youtube> | <youtube>zQy02LsARo0</youtube> | ||
<youtube>K5MlmO0UJtI</youtube> | <youtube>K5MlmO0UJtI</youtube> | ||
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<youtube>ARfpQzRCWT4</youtube> | <youtube>ARfpQzRCWT4</youtube> | ||
| − | + | https://thegradient.pub/content/images/2019/03/image44.png | |
== HIerarchical Reinforcement learning with Off-policy correction (HIRO) == | == HIerarchical Reinforcement learning with Off-policy correction (HIRO) == | ||
| − | * [ | + | * [https://towardsdatascience.com/advanced-reinforcement-learning-6d769f529eb3 Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning | Joyce Xu - Towards Data Science] |
| − | * [ | + | * [https://arxiv.org/pdf/1805.08296.pdf Data-Efficient Hierarchical Reinforcement Learning | O. Nachum, S. Gu, H. Lee, and S. Levine - Google Brain] |
HIRO can be used to learn highly complex behaviors for simulated robots, such | HIRO can be used to learn highly complex behaviors for simulated robots, such | ||
as pushing objects and utilizing them to reach target locations, learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods. | as pushing objects and utilizing them to reach target locations, learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods. | ||
| − | + | <youtube>yLHzDky2ApI</youtube> | |
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| + | https://miro.medium.com/max/678/1*Fq-TQ7Mu2XDOIZ6R7dkRjw.png | ||
Latest revision as of 15:35, 16 April 2023
Youtube search... ...Google search
- The Promise of Hierarchical Reinforcement Learning | Yannis Flet-Berliac - The Gradient
- Hierarchical Reinforcement Learning | David Jardim
- 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
- Hierarchical Reinforcement Learning (HRL)
- Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)
HRL is a promising approach to extend traditional Reinforcement Learning (RL) methods to solve more complex tasks.
HIerarchical Reinforcement learning with Off-policy correction (HIRO)
- Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning | Joyce Xu - Towards Data Science
- Data-Efficient Hierarchical Reinforcement Learning | O. Nachum, S. Gu, H. Lee, and S. Levine - Google Brain
HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations, learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods.
