Reinforcement Learning - Revision history

EtcetFelix: /* Resources */

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‎Resources

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‎Add MuJoCo

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‎Add work in progress tag at top

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‎Adding Isaac related details to Reinforcement Learning (RL)

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 * [https://cs224r.stanford.edu/slides/cs224r-actor-critic-split.pdf Stanford CS224R Actor Critic Slides]
 [[Category: Software]]
 [[Category: Reinforcement Learning]]

← Older revision		Revision as of 03:11, 21 May 2024
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	[[Category: Software]]		[[Category: Software]]
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 * Isaac Sim provides a highly realistic and interactive environment where robots can be trained safely and efficiently.
 * The simulated environment includes physics, sensors, and other elements that mimic real-world conditions, enabling the transfer of learned behaviors to physical robots.
 == Training algorithms ==

← Older revision		Revision as of 20:03, 16 May 2024
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		+	This guide is incomplete and a work in progress; you can help by expanding it!
		+
	== Reinforcement Learning (RL) ==		== Reinforcement Learning (RL) ==
	Reinforcement Learning (RL) is a machine learning approach where an agent learns to perform tasks by interacting with an environment. It involves the agent receiving rewards or penalties based on its actions and using this feedback to improve its performance over time. RL is particularly useful in robotics for training robots to perform complex tasks autonomously. Here's how RL is applied in robotics, using simulation environments like Isaac Sim and MuJoCo:		Reinforcement Learning (RL) is a machine learning approach where an agent learns to perform tasks by interacting with an environment. It involves the agent receiving rewards or penalties based on its actions and using this feedback to improve its performance over time. RL is particularly useful in robotics for training robots to perform complex tasks autonomously. Here's how RL is applied in robotics, using simulation environments like Isaac Sim and MuJoCo:

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 ==== Navigation and Manipulation ====
 * RL is used to train robots for navigating complex environments and manipulating objects with precision, which is crucial for tasks like warehouse logistics, domestic chores, and medical surgeries.
 == Training algorithms ==

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 * Robots can be trained to perform repetitive or dangerous tasks autonomously, such as assembly line work, welding, or hazardous material handling.
 * RL enables robots to adapt to new tasks without extensive reprogramming, making them versatile for various industrial applications.
 == Training algorithms ==

← Older revision		Revision as of 19:57, 16 May 2024
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	Reinforcement Learning (RL) is a machine learning approach where an agent learns to perform tasks by interacting with an environment. It involves the agent receiving rewards or penalties based on its actions and using this feedback to improve its performance over time. RL is particularly useful in robotics for training robots to perform complex tasks autonomously. Here's how RL is applied in robotics, using simulation environments like Isaac Sim and MuJoCo:		Reinforcement Learning (RL) is a machine learning approach where an agent learns to perform tasks by interacting with an environment. It involves the agent receiving rewards or penalties based on its actions and using this feedback to improve its performance over time. RL is particularly useful in robotics for training robots to perform complex tasks autonomously. Here's how RL is applied in robotics, using simulation environments like Isaac Sim and MuJoCo:

		+	== RL in Robotics ==
		+	=== Practical Applications of RL ===
		+	==== Task Automation ====
		+	* Robots can be trained to perform repetitive or dangerous tasks autonomously, such as assembly line work, welding, or hazardous material handling.
		+	* RL enables robots to adapt to new tasks without extensive reprogramming, making them versatile for various industrial applications.

	== Training algorithms ==		== Training algorithms ==

← Older revision		Revision as of 19:56, 16 May 2024
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	== Reinforcement Learning (RL) ==		== Reinforcement Learning (RL) ==
−	Reinforcement Learning (RL) is a machine learning approach where an agent learns to perform tasks by interacting with an environment. It involves the agent receiving rewards or penalties based on its actions, and using this feedback to improve its performance over time.	+	Reinforcement Learning (RL) is a machine learning approach where an agent learns to perform tasks by interacting with an environment. It involves the agent receiving rewards or penalties based on its actions and using this feedback to improve its performance over time. RL is particularly useful in robotics for training robots to perform complex tasks autonomously. Here's how RL is applied in robotics, using simulation environments like Isaac Sim and MuJoCo:
−

← Older revision		Revision as of 19:54, 16 May 2024
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		+	== Reinforcement Learning (RL) ==
		+	Reinforcement Learning (RL) is a machine learning approach where an agent learns to perform tasks by interacting with an environment. It involves the agent receiving rewards or penalties based on its actions, and using this feedback to improve its performance over time.
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		+
		+
	== Training algorithms ==		== Training algorithms ==

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 == Training algorithms ==
-* [https://en.wikipedia.org/wiki/Advantage_Actor_Critic A2C] (also see slides on Actor Critic methods at [1])
+* [https://en.wikipedia.org/wiki/Advantage_Actor_Critic A2C]
 * [https://en.wikipedia.org/wiki/Proximal_policy_optimization PPO]
 * [https://spinningup.openai.com/en/latest/algorithms/sac.html SAC]
 == Resources ==
 * [https://mandi-zhao.gitbook.io/deeprl-notes Mandy Zhao's Reinforcement Learning Notes]
 [[Category: Software]]