Hierarchical Reinforcement Learning (HRL) - Revision history

BPeat at 20:35, 16 April 2023

2023-04-16T20:35:27Z

BPeat: Text replacement - "http://" to "https://"

2023-03-28T22:05:48Z

Text replacement - "http://" to "https://"

BPeat at 11:17, 6 July 2020

2020-07-06T11:17:42Z

BPeat at 11:17, 6 July 2020

2020-07-06T11:17:17Z

BPeat at 16:51, 3 July 2020

2020-07-03T16:51:09Z

BPeat at 00:46, 2 September 2019

2019-09-02T00:46:10Z

BPeat at 21:23, 1 September 2019

2019-09-01T21:23:58Z

BPeat at 20:44, 1 September 2019

2019-09-01T20:44:43Z

BPeat: /* HIerarchical Reinforcement learning with Off-policy correction (HIRO) */

2019-09-01T20:41:52Z

‎HIerarchical Reinforcement learning with Off-policy correction (HIRO)

BPeat at 20:32, 1 September 2019

2019-09-01T20:32:38Z

← Older revision		Revision as of 20:35, 16 April 2023
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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)]]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Hierarchical+Reinforcement+Learning Youtube search...]
+[https://www.youtube.com/results?search_query=Hierarchical+Reinforcement+Learning Youtube search...]
-[http://www.google.com/search?q=Hierarchical+Reinforcement+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Hierarchical+Reinforcement+machine+learning+ML+artificial+intelligence ...Google search]
-* [http://thegradient.pub/the-promise-of-hierarchical-reinforcement-learning The Promise of Hierarchical Reinforcement Learning | Yannis Flet-Berliac - The Gradient]
+* [https://thegradient.pub/the-promise-of-hierarchical-reinforcement-learning The Promise of Hierarchical Reinforcement Learning | Yannis Flet-Berliac - The Gradient]
-* [http://www.slideshare.net/DavidJardim/hierarchical-reinforcement-learning Hierarchical Reinforcement Learning | David Jardim]
+* [https://www.slideshare.net/DavidJardim/hierarchical-reinforcement-learning Hierarchical Reinforcement Learning | David Jardim]
 * [[Reinforcement Learning (RL)]]
@@ Line 35: / Line 35: @@
 <youtube>ARfpQzRCWT4</youtube>
-http://thegradient.pub/content/images/2019/03/image44.png
+https://thegradient.pub/content/images/2019/03/image44.png
 == HIerarchical Reinforcement learning with Off-policy correction (HIRO) ==
-* [http://towardsdatascience.com/advanced-reinforcement-learning-6d769f529eb3 Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning | Joyce Xu - Towards Data Science]
+* [https://towardsdatascience.com/advanced-reinforcement-learning-6d769f529eb3 Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning | Joyce Xu - Towards Data Science]
-* [http://arxiv.org/pdf/1805.08296.pdf Data-Efficient Hierarchical Reinforcement Learning | O. Nachum, S. Gu, H. Lee, and S. Levine - Google Brain]
+* [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
@@ Line 46: / Line 46: @@
 <youtube>yLHzDky2ApI</youtube>
-http://miro.medium.com/max/678/1*Fq-TQ7Mu2XDOIZ6R7dkRjw.png
+https://miro.medium.com/max/678/1*Fq-TQ7Mu2XDOIZ6R7dkRjw.png

@@ Line 30: / Line 30: @@
 <youtube>x_QjJry0hTc</youtube>
 <youtube>zQy02LsARo0</youtube>
 <youtube>K5MlmO0UJtI</youtube>

@@ Line 10: / Line 10: @@
 * [http://thegradient.pub/the-promise-of-hierarchical-reinforcement-learning The Promise of Hierarchical Reinforcement Learning | Yannis Flet-Berliac - The Gradient]
 * [http://www.slideshare.net/DavidJardim/hierarchical-reinforcement-learning Hierarchical Reinforcement Learning | David Jardim]
-* [[Reinforcement Learning (RL)]]:
 ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning
 ** [[Markov Decision Process (MDP)]]
 ** [[Q Learning]]
-** [[State-Action-Reward-State-Action (SARSA)]]
+*** [[Deep Q Network (DQN)]]
 ** [[Deep Reinforcement Learning (DRL)]] DeepRL
 ** [[Distributed Deep Reinforcement Learning (DDRL)]]
 ** [[Evolutionary Computation / Genetic Algorithms]]
 ** [[Actor Critic]]
 *** [[Advanced Actor Critic (A2C)]]
 *** [[Lifelong Latent Actor-Critic (LILAC)]]
 ** Hierarchical Reinforcement Learning (HRL)
-Hierarchical reinforcement learning (HRL) is a promising approach to extend
-traditional [[Reinforcement Learning (RL)]] methods to solve more complex tasks.
+HRL is a promising approach to extend traditional [[Reinforcement Learning (RL)]] methods to solve more complex tasks.
 <youtube>x_QjJry0hTc</youtube>

@@ Line 20: / Line 20: @@
 ** [[Evolutionary Computation / Genetic Algorithms]]
 ** [[Actor Critic]]
 Hierarchical reinforcement learning (HRL) is a promising approach to extend

← Older revision		Revision as of 20:41, 1 September 2019
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	== HIerarchical Reinforcement learning with Off-policy correction (HIRO) ==		== HIerarchical Reinforcement learning with Off-policy correction (HIRO) ==
		+	* [http://towardsdatascience.com/advanced-reinforcement-learning-6d769f529eb3 Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning \| Joyce Xu - Towards Data Science]
		+	* [http://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
		+	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.

	http://miro.medium.com/max/678/1*Fq-TQ7Mu2XDOIZ6R7dkRjw.png		http://miro.medium.com/max/678/1*Fq-TQ7Mu2XDOIZ6R7dkRjw.png