From Reinforcement Learning Towards Artificial General Intelligence

2020 ◽  
pp. 401-413
Author(s):  
Filipe Marinho Rocha ◽  
Vítor Santos Costa ◽  
Luís Paulo Reis
Keyword(s):  
Reinforcement Learning ◽  
General Intelligence ◽  
Artificial General Intelligence

scholarly journals The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI

2020 ◽  
Vol 11 (1) ◽  
pp. 70-85
Author(s):  
Samuel Allen Alexander
Keyword(s):  
Reinforcement Learning ◽  
General Intelligence ◽  
Real Numbers ◽  
The Real ◽  
Artificial General Intelligence ◽  
Archimedean Property

AbstractAfter generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two possible ways traditional reinforcement learning could be altered to remove this roadblock.

scholarly journals OODA-RL: A REINFORCEMENT LEARNING FRAMEWORK FOR ARTIFICIAL GENERAL INTELLIGENCE TO SOLVE OPEN WORLD NOVELTY

2021 ◽  
Author(s):  
Pamul Yadav ◽  
Taewoo Kim ◽  
Ho Suk ◽  
Junyong Lee ◽  
Hyeonseong Jeong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Intelligent Agents ◽  
General Intelligence ◽  
Learning Framework ◽  
Artificial General Intelligence ◽  
Open World ◽  
Definition Of

<p>Faster adaptability to open-world novelties by intelligent agents is a necessary factor in achieving the goal of creating Artificial General Intelligence (AGI). Current RL framework does not considers the unseen changes (novelties) in the environment. Therefore, in this paper, we have proposed OODA-RL, a Reinforcement Learning based framework that can be used to develop robust RL algorithms capable of handling both the known environments as well as adaptation to the unseen environments. OODA-RL expands the definition of internal composition of the agent as compared to the abstract definition in the classical RL framework, allowing the RL researchers to incorporate novelty adaptation techniques as an add-on feature to the existing SoTA as well as yet-to-be-developed RL algorithms.</p>

scholarly journals OODA-RL: A Reinforcement Learning Framework For Artificial General Intelligence To Solve Open World Novelty

2021 ◽  
Author(s):  
Pamul Yadav ◽  
Taewoo Kim ◽  
Ho Suk ◽  
Junyong Lee ◽  
Hyeonseong Jeong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Intelligent Agents ◽  
General Intelligence ◽  
Learning Framework ◽  
Artificial General Intelligence ◽  
Open World ◽  
Definition Of

<p>Faster adaptability to open-world novelties by intelligent agents is a necessary factor in achieving the goal of creating Artificial General Intelligence (AGI). Current RL framework does not considers the unseen changes (novelties) in the environment. Therefore, in this paper, we have proposed OODA-RL, a Reinforcement Learning based framework that can be used to develop robust RL algorithms capable of handling both the known environments as well as adaptation to the unseen environments. OODA-RL expands the definition of internal composition of the agent as compared to the abstract definition in the classical RL framework, allowing the RL researchers to incorporate novelty adaptation techniques as an add-on feature to the existing SoTA as well as yet-to-be-developed RL algorithms.</p>

scholarly journals OODA-RL: A Reinforcement Learning Framework For Artificial General Intelligence To Solve Open World Novelty

2021 ◽  
Author(s):  
Pamul Yadav ◽  
Taewoo Kim ◽  
Ho Suk ◽  
Junyong Lee ◽  
Hyeonseong Jeong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Intelligent Agents ◽  
General Intelligence ◽  
Learning Framework ◽  
Artificial General Intelligence ◽  
Open World ◽  
Definition Of

<p>Faster adaptability to open-world novelties by intelligent agents is a necessary factor in achieving the goal of creating Artificial General Intelligence (AGI). Current RL framework does not considers the unseen changes (novelties) in the environment. Therefore, in this paper, we have proposed OODA-RL, a Reinforcement Learning based framework that can be used to develop robust RL algorithms capable of handling both the known environments as well as adaptation to the unseen environments. OODA-RL expands the definition of internal composition of the agent as compared to the abstract definition in the classical RL framework, allowing the RL researchers to incorporate novelty adaptation techniques as an add-on feature to the existing SoTA as well as yet-to-be-developed RL algorithms.</p>

Who is in control? Managerial artificial general intelligence (MAGI) for Football

2021 ◽  
pp. 1-6
Author(s):  
Scott McLean ◽  
Gemma J. M. Read ◽  
Jason Thompson ◽  
P. A. Hancock ◽  
Paul M. Salmon
Keyword(s):  
General Intelligence ◽  
Artificial General Intelligence
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