Q Learning - Revision history

BPeat at 16:59, 6 November 2024

2024-11-06T16:59:56Z

BPeat at 01:15, 20 March 2024

2024-03-20T01:15:36Z

BPeat at 19:50, 4 July 2023

2023-07-04T19:50:19Z

BPeat at 01:23, 24 May 2023

2023-05-24T01:23:55Z

BPeat: /* What is Q Learning */

2023-05-24T01:23:12Z

‎What is Q Learning

BPeat: /* What is Q Learning */

2023-05-24T01:22:51Z

‎What is Q Learning

BPeat: /* Q Learning for Gaming */

2023-05-24T01:22:13Z

‎Q Learning for Gaming

BPeat: /* Q Learning for Gaming */

2023-05-24T01:20:20Z

‎Q Learning for Gaming

BPeat: /* Q Learning for Gaming */

2023-05-24T01:16:22Z

‎Q Learning for Gaming

BPeat: /* Q Learning for Gaming */

2023-05-24T01:15:05Z

‎Q Learning for Gaming

@@ Line 70: / Line 70: @@
 = Q Learning for Gaming =
-* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]]
+* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
 * [https://www.practicalai.io/teaching-a-neural-network-to-play-a-game-with-q-learning Teaching a Neural Network to play a game using Q-learning]
 * [https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a How to teach AI to play Games: Deep Reinforcement Learning]

@@ Line 70: / Line 70: @@
 = Q Learning for Gaming =
-* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]]
+* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]]
 * [https://www.practicalai.io/teaching-a-neural-network-to-play-a-game-with-q-learning Teaching a Neural Network to play a game using Q-learning]
 * [https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a How to teach AI to play Games: Deep Reinforcement Learning]

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 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |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
 }}
 [http://www.youtube.com/results?search_query=deep+reinforcement+q+learning+artificial+intelligence+ Youtube search...]
@@ Line 10: / Line 19: @@
 * [http://en.wikipedia.org/wiki/Q-learning Q Learning | Wikipedia]
 * [http://medium.com/deep-math-machine-learning-ai/ch-12-1-model-free-reinforcement-learning-algorithms-monte-carlo-sarsa-q-learning-65267cb8d1b4 Model Free Reinforcement learning algorithms (Monte Carlo, SARSA, Q-learning) | Madhu Sanjeevi (Mady) - Medium]
-* [[Reinforcement Learning (RL)]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning
 ** [[Markov Decision Process (MDP)]]

← Older revision		Revision as of 01:23, 24 May 2023
Line 25:		Line 25:
	** [[Hierarchical Reinforcement Learning (HRL)]]		** [[Hierarchical Reinforcement Learning (HRL)]]
	* [[Gaming]]		* [[Gaming]]
−
−

@@ Line 42: / Line 42: @@
 = What is Q Learning =
 What is Q-learning?
-Q-learning is a machine learning approach that enables a model to iteratively learn and improve over time by taking the correct action. Q-learning is a type of reinforcement learning.
+Q-learning is a machine learning approach that enables a model to iteratively learn and improve over time by taking the correct action. Q-learning is a type of reinforcement learning. With reinforcement learning, a machine learning model is trained to mimic the way animals or children learn. Good actions are rewarded or reinforced, while bad actions are discouraged and penalized. With the state-action-reward-state-action form of reinforcement learning, the training regimen follows a model to take the right actions. Q-learning provides a model-free approach to reinforcement learning. There is no model of the environment to guide the reinforcement learning process. The agent -- which is the AI component that acts in the environment -- iteratively learns and makes predictions about the environment on its own. Q-learning also takes an off-policy approach to reinforcement learning. A Q-learning approach aims to determine the optimal action based on its current state. The Q-learning approach can accomplish this by either developing its own set of rules or deviating from the prescribed policy. Because Q-learning may deviate from the given policy, a defined policy is not needed. Off-policy approach in Q-learning is achieved using Q-values -- also known as action values. The Q-values are the expected future values for action and are stored in the Q-table. Chris Watkins first discussed the foundations of Q-learning in a 1989 thesis for Cambridge University and further elaborated in a 1992 publication titled Q-learning.
 How does Q-learning work?

← Older revision		Revision as of 01:22, 24 May 2023
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	* [https://stackoverflow.com/questions/49451366/q-learning-applied-to-a-two-player-game Q Learning Applied To a Two Player Game \| Stack Overflow]		* [https://stackoverflow.com/questions/49451366/q-learning-applied-to-a-two-player-game Q Learning Applied To a Two Player Game \| Stack Overflow]

−	Q-learning can be applied to gaming by teaching a neural network to play a game using Q-learning¹. The Q-learning algorithm uses a Q-table to look up the optimal next action based on the current state of the ~~game¹~~. However, as the complexity of the game grows, so does the Q-table. One alternative approach is to replace the table lookup with a neural network that takes a state and an action as input and outputs the Q-value, which represents the possible reward for taking that action at that state.	+	Q-learning can be applied to gaming by teaching a neural network to play a game using Q-learning¹. The Q-learning algorithm uses a Q-table to look up the optimal next action based on the current state of the game. However, as the complexity of the game grows, so does the Q-table. One alternative approach is to replace the table lookup with a neural network that takes a state and an action as input and outputs the Q-value, which represents the possible reward for taking that action at that state. In Q-learning, the agent uses a Q-table to take the best possible action based on the expected reward for each state in the environment. A Q-table is a data structure of sets of actions and states, and the Q-learning algorithm is used to update the values in the table1. The Q-table score for each state-action pair represents the maximum expected future reward that the agent will receive if it takes that action at that state.

← Older revision		Revision as of 01:20, 24 May 2023
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	= Q Learning for Gaming =		= Q Learning for Gaming =
	* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security\|Security]] ... [[Game Development with Generative AI\|Generative AI]] ... [[Metaverse#Games - Metaverse\|Metaverse]] ... [[Games - Quantum Theme\|Quantum]] ... [[Game Theory]]		* [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security\|Security]] ... [[Game Development with Generative AI\|Generative AI]] ... [[Metaverse#Games - Metaverse\|Metaverse]] ... [[Games - Quantum Theme\|Quantum]] ... [[Game Theory]]
		+	* [https://www.practicalai.io/teaching-a-neural-network-to-play-a-game-with-q-learning Teaching a Neural Network to play a game using Q-learning]
		+	* [https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a How to teach AI to play Games: Deep Reinforcement Learning]
		+	* [https://stackoverflow.com/questions/49451366/q-learning-applied-to-a-two-player-game Q Learning Applied To a Two Player Game \| Stack Overflow]
		+
		+	Q-learning can be applied to gaming by teaching a neural network to play a game using Q-learning¹. The Q-learning algorithm uses a Q-table to look up the optimal next action based on the current state of the game¹. However, as the complexity of the game grows, so does the Q-table. One alternative approach is to replace the table lookup with a neural network that takes a state and an action as input and outputs the Q-value, which represents the possible reward for taking that action at that state.

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