Deep Reinforcement Learning (DRL) - Revision history

BPeat at 20:29, 16 April 2023

2023-04-16T20:29:38Z

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

2023-03-28T13:09:49Z

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

BPeat at 13:01, 4 February 2023

2023-02-04T13:01:09Z

BPeat at 17:58, 15 August 2020

2020-08-15T17:58:01Z

BPeat at 11:19, 6 July 2020

2020-07-06T11:19:44Z

BPeat at 11:09, 6 July 2020

2020-07-06T11:09:01Z

BPeat at 16:49, 3 July 2020

2020-07-03T16:49:13Z

BPeat at 01:33, 2 September 2019

2019-09-02T01:33:44Z

BPeat at 01:27, 2 September 2019

2019-09-02T01:27:35Z

BPeat at 01:25, 2 September 2019

2019-09-02T01:25:48Z

@@ Line 24: / Line 24: @@
 * [[Inside Out - Curious Optimistic Reasoning#MERLIN|MERLIN]]: [[Inside Out - Curious Optimistic Reasoning]]
 * [[OpenAI#OpenAI Gym | OpenAI Gym]]
+* [[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=deep+reinforcement+learning Youtube search...]
+[https://www.youtube.com/results?search_query=deep+reinforcement+learning Youtube search...]
-[http://www.google.com/search?q=reinforcement+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=reinforcement+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Reinforcement Learning (RL)]]
@@ Line 35: / Line 35: @@
 * [https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-i-q-learning-sarsa-dqn-ddpg-72a5e0cb6287 Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG) | Steeve Huang]
 * [https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-part-ii-trpo-ppo-87f2c5919bb9 Introduction to Various Reinforcement Learning Algorithms. Part II (TRPO, PPO) | Steeve Huang]
-* [http://deeplearning4j.org/deepreinforcementlearning.html Guide]
+* [https://deeplearning4j.org/deepreinforcementlearning.html Guide]
 https://upload.wikimedia.org/wikipedia/commons/thumb/1/1b/Reinforcement_learning_diagram.svg/375px-Reinforcement_learning_diagram.svg.png
@@ Line 50: / Line 50: @@
 == Importance Weighted Actor-Learner Architecture (IMPALA) ==
-[http://www.youtube.com/results?search_query=Impala+AGI+machine+artificial+intelligence+deep+learning+simple YouTube search...]
+[https://www.youtube.com/results?search_query=Impala+AGI+machine+artificial+intelligence+deep+learning+simple YouTube search...]
-[http://www.google.com/search?q=Impala+AGIb+deep+machine+learning+ML ...Google search]
+[https://www.google.com/search?q=Impala+AGIb+deep+machine+learning+ML ...Google search]
-* [http://www.extremetech.com/extreme/275768-artificial-general-intelligence-is-here-and-impala-is-its-nameDeepMind Artificial General Intelligence Is Here, and Impala Is Its Name | Aaron Krumins]
+* [https://www.extremetech.com/extreme/275768-artificial-general-intelligence-is-here-and-impala-is-its-nameDeepMind Artificial General Intelligence Is Here, and Impala Is Its Name | Aaron Krumins]
-* [http://deepmind.com/blog/open-sourcing-deepmind-lab/ DeepMind Lab]
+* [https://deepmind.com/blog/open-sourcing-deepmind-lab/ DeepMind Lab]
-* [http://deepmind.com/research/publications/impala-scalable-distributed-deep-rl-importance-weighted-actor-learner-architectures/ IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures]
+* [https://deepmind.com/research/publications/impala-scalable-distributed-deep-rl-importance-weighted-actor-learner-architectures/ IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures]
-* [http://deepmind.com/blog/impala-scalable-distributed-deeprl-dmlab-30/  Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30]
+* [https://deepmind.com/blog/impala-scalable-distributed-deeprl-dmlab-30/  Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30]
 uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. IMPALA is able to achieve better performance than previous [[agents]] with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach.
-http://ai2-s2-public.s3.amazonaws.com/figures/2017-08-08/cbddc41e5b07ad8e3f7982e232bafba84c8419cc/5-Figure3-1.png
+https://ai2-s2-public.s3.amazonaws.com/figures/2017-08-08/cbddc41e5b07ad8e3f7982e232bafba84c8419cc/5-Figure3-1.png
@@ Line 68: / Line 68: @@
 == DMLab-30 ==
-[http://www.youtube.com/results?search_query=DMLab-30+Distributed+Deep+Reinforcement+Learning+DeepRL Youtube search...]
+[https://www.youtube.com/results?search_query=DMLab-30+Distributed+Deep+Reinforcement+Learning+DeepRL Youtube search...]
-[http://www.google.com/search?q=Distributed+DMLab-30+Deep+Reinforcement+Learning+DeepRL+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Distributed+DMLab-30+Deep+Reinforcement+Learning+DeepRL+machine+learning+ML+artificial+intelligence ...Google search]
 DMLab-30 is a collection of new levels designed using our open source RL environment DeepMind Lab. These environments enable any DeepRL researcher to test systems on a large spectrum of interesting tasks either individually or in a multi-task setting.
-* [http://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30 DMLab-30 | GitHub]
+* [https://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30 DMLab-30 | GitHub]
-* [http://www.techleer.com/articles/488-impala-distributed-agent-in-dmlab-30/ IMPALA distributed] [[Agents|agent]] in DMLab-30
+* [https://www.techleer.com/articles/488-impala-distributed-agent-in-dmlab-30/ IMPALA distributed] [[Agents|agent]] in DMLab-30
-* [http://www.semanticscholar.org/paper/IMPALA%3A-Scalable-Distributed-Deep-RL-with-Weighted-Espeholt-Soyer/cbddc41e5b07ad8e3f7982e232bafba84c8419cc IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures]
+* [https://www.semanticscholar.org/paper/IMPALA%3A-Scalable-Distributed-Deep-RL-with-Weighted-Espeholt-Soyer/cbddc41e5b07ad8e3f7982e232bafba84c8419cc IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures]
-http://s3.ap-south-1.amazonaws.com/techleerimages/4d62b60c-4dcd-4774-9c75-417eba1cbbc1.png
+https://s3.ap-south-1.amazonaws.com/techleerimages/4d62b60c-4dcd-4774-9c75-417eba1cbbc1.png

@@ Line 58: / Line 58: @@
 * [http://deepmind.com/blog/impala-scalable-distributed-deeprl-dmlab-30/  Importance Weighted Actor-Learner Architectures: Scalable Distributed DeepRL in DMLab-30]
-uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach.
+uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. IMPALA is able to achieve better performance than previous [[agents]] with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach.
 http://ai2-s2-public.s3.amazonaws.com/figures/2017-08-08/cbddc41e5b07ad8e3f7982e232bafba84c8419cc/5-Figure3-1.png
@@ Line 74: / Line 74: @@
 * [http://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30 DMLab-30 | GitHub]
-* [http://www.techleer.com/articles/488-impala-distributed-agent-in-dmlab-30/ IMPALA distributed agent in DMLab-30]
+* [http://www.techleer.com/articles/488-impala-distributed-agent-in-dmlab-30/ IMPALA distributed] [[Agents|agent]] in DMLab-30
 * [http://www.semanticscholar.org/paper/IMPALA%3A-Scalable-Distributed-Deep-RL-with-Weighted-Espeholt-Soyer/cbddc41e5b07ad8e3f7982e232bafba84c8419cc IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures]
 http://s3.ap-south-1.amazonaws.com/techleerimages/4d62b60c-4dcd-4774-9c75-417eba1cbbc1.png

@@ Line 22: / Line 22: @@
 *** [[Lifelong Latent Actor-Critic (LILAC)]]
 ** [[Hierarchical Reinforcement Learning (HRL)]]
 * [[Inside Out - Curious Optimistic Reasoning#MERLIN|MERLIN]]: [[Inside Out - Curious Optimistic Reasoning]]
-* [[OpenAI Gym]]
+* [[OpenAI#OpenAI Gym | OpenAI Gym]]
 ==== OTHER: Policy Gradient Methods ====

@@ Line 18: / Line 18: @@
 ** [[Evolutionary Computation / Genetic Algorithms]]
 ** [[Actor Critic]]
 *** [[Advanced Actor Critic (A2C)]]
 *** [[Lifelong Latent Actor-Critic (LILAC)]]
 ** [[Hierarchical Reinforcement Learning (HRL)]]
 * [[Inside Out - Curious Optimistic Reasoning#MERLIN|MERLIN]]: [[Inside Out - Curious Optimistic Reasoning]]
 * [[OpenAI Gym]]

@@ Line 11: / Line 11: @@
 ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning
 ** [[Markov Decision Process (MDP)]]
 ** [[Q Learning]]
-** [[State-Action-Reward-State-Action (SARSA)]]
+*** [[Deep Q Network (DQN)]]
 ** [[Distributed Deep Reinforcement Learning (DDRL)]]
 ** [[Evolutionary Computation / Genetic Algorithms]]
 ** [[Actor Critic]]

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 <youtube>mckulxKWyoc</youtube>
 <youtube>eYlJsDH7ggE</youtube>

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 <youtube>e3Jy2vShroE</youtube>
 <youtube>lvoHnicueoE</youtube>
 <youtube>MQ6pP65o7OM</youtube>