Imitation Learning (IL) - Revision history

BPeat at 19:19, 16 March 2024

2024-03-16T19:19:04Z

BPeat: Text replacement - "* Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu" to "* Conversational AI ... [[C...

2024-03-16T15:21:34Z

BPeat at 17:15, 16 September 2023

2023-09-16T17:15:00Z

BPeat at 00:03, 9 September 2023

2023-09-09T00:03:03Z

BPeat at 19:54, 2 September 2023

2023-09-02T19:54:45Z

BPeat: BPeat moved page Imitation Learning to Imitation Learning (IL) without leaving a redirect

2023-08-13T11:46:57Z

BPeat moved page Imitation Learning to Imitation Learning (IL) without leaving a redirect

BPeat at 11:38, 13 August 2023

2023-08-13T11:38:10Z

BPeat at 11:37, 13 August 2023

2023-08-13T11:37:40Z

BPeat: /* Conditional Adversarial Latent Model (CALM) */

2023-08-13T11:35:03Z

‎Conditional Adversarial Latent Model (CALM)

BPeat at 11:27, 13 August 2023

2023-08-13T11:27:29Z

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 |title=PRIMO.ai
 |titlemode=append
-|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
+|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, Gemini, 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
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 * [[Supervised|Supervised Learning]] ... [[Semi-Supervised]] ... [[Self-Supervised]] ... [[Unsupervised]]
 * [[Feature Exploration/Learning]]
-* [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 * [[Context-Conditional Generative Adversarial Network (CC-GAN)]]
 * [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]]

@@ Line 26: / Line 26: @@
 * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[Attention]] Mechanism  ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
-* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]

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 * [[Context-Conditional Generative Adversarial Network (CC-GAN)]]
 * [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]]
 * [https://research.nvidia.com/labs/par/calm/ CALM: Conditional Adversarial Latent Models for Directable Virtual Characters | C. Tessler, Y. Kasten, Y. Guo, S. Mannor, G. Chechik, X. Bin Peng - NVIDIA]
-a [[Machine Learning (ML)|machine learning approach]] for generating diverse and directable behaviors for user-controlled interactive virtual characters. CALM works by first learning a representation of movement that captures the complexity and diversity of human motion. This is done using imitation learning, where CALM is trained on a dataset of human motion capture data. The learned representation is a latent space, which is a high-dimensional space that captures the essential features of human motion. Once the latent space has been learned, CALM can be used to generate new motions by sampling from the latent space and then decoding the samples into motion trajectories. The generated motions can be controlled by adjusting the latent code. This allows users to control the character's movements in a direct and intuitive way. CALM also has the ability to condition the generated motions on a variety of factors, such as the character's goal, the environment, or the character's personality. This allows CALM to generate motions that are appropriate for the given context.
+a [[Machine Learning (ML)|machine learning approach]] for generating diverse and directable behaviors for user-controlled interactive virtual characters. CALM works by first learning a representation of movement that captures the complexity and diversity of human motion. This is done using imitation learning, where CALM is trained on a dataset of human motion capture data. The learned representation is a [[latent]] space, which is a high-dimensional space that captures the essential features of human motion. Once the [[latent]] space has been learned, CALM can be used to generate new motions by sampling from the [[latent]] space and then decoding the samples into motion trajectories. The generated motions can be controlled by adjusting the [[latent]] code. This allows users to control the character's movements in a direct and intuitive way. CALM also has the ability to condition the generated motions on a variety of factors, such as the character's goal, the environment, or the character's personality. This allows CALM to generate motions that are appropriate for the given context.
 CALM has been shown to be effective in generating diverse and directable behaviors for user-controlled interactive virtual characters. It has been used to create virtual characters that can walk, run, jump, dance, and perform other actions. CALM has also been used to create virtual characters that can interact with objects in the environment and follow instructions. CALM is a promising new approach for generating realistic and engaging virtual characters. It has the potential to revolutionize the way that virtual characters are created and used in games, movies, and other applications.

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 ** [[Apprenticeship Learning - Inverse Reinforcement Learning (IRL)]]
 ** [[Transfer Learning]]
-* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]]
+* [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[Attention]] Mechanism  ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]

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 * [[Learning Techniques]]
-** [[Reinforcement Learning (RL)]]
+** [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 ** [[Apprenticeship Learning - Inverse Reinforcement Learning (IRL)]]
 ** [[Transfer Learning]]
 * [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[Attention]] Mechanism  ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
-* [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]

← Older revision	Revision as of 11:46, 13 August 2023
(No difference)

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 * <b>Behavioral cloning (BC)</b>: is the simplest form of imitation learning. In BC, the agent learns a mapping from states to actions by simply copying the actions of the expert in each state. BC is easy to implement, but it can be brittle if the expert's behavior is not perfect.
-* <b>Inverse reinforcement learning (IRL)</b>: is a more complex approach to imitation learning. In IRL, the agent learns a reward function that explains the expert's behavior. Once the reward function is learned, the agent can then use it to find its own policy that maximizes the reward. IRL is more robust to noise in the expert's behavior than BC, but it is also more computationally expensive to train.
+* <b>[[Apprenticeship Learning - Inverse Reinforcement Learning (IRL)]]</b>: is a more complex approach to imitation learning. In IRL, the agent learns a reward function that explains the expert's behavior. Once the reward function is learned, the agent can then use it to find its own policy that maximizes the reward. IRL is more robust to noise in the expert's behavior than BC, but it is also more computationally expensive to train.
 Here are some other approaches to imitation learning:
 * <b>DAgger (Dataset Aggregation)</b>: is a hybrid approach that combines BC and IRL. In DAgger, the agent starts by learning a policy using BC. Then, it uses this policy to collect new data from the environment. This new data is then used to improve the policy, and the process repeats.
-* <b>Proximal Policy Optimization with Imitation Learning (PPO-IL)</b>: is a reinforcement learning algorithm that can be used for imitation learning. PPO-IL learns a policy that is close to the expert's policy, while also ensuring that the policy is still able to learn from its own experience.
+* <b>[[Proximal Policy Optimization (PPO)#Proximal Policy Optimization with Imitation Learning (PPO-IL) |Proximal Policy Optimization with Imitation Learning (PPO-IL)]]</b>: is a reinforcement learning algorithm that can be used for imitation learning. PPO-IL learns a policy that is close to the expert's policy, while also ensuring that the policy is still able to learn from its own experience.
-* <b>Transfer learning</b>: can be used to improve the performance of imitation learning algorithms. Transfer learning involves using a model that has been trained on a related task to help train a model for a new task. This can be helpful in imitation learning, because it can help the agent to generalize to new situations.
+* <b>[[Transfer Learning]]</b>: can be used to improve the performance of imitation learning algorithms. Transfer learning involves using a model that has been trained on a related task to help train a model for a new task. This can be helpful in imitation learning, because it can help the agent to generalize to new situations.

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-= Conditional Adversarial Latent Model (CALM) =
+= <span id="Conditional Adversarial Latent Model (CALM)"></span>Conditional Adversarial Latent Model (CALM) =
 [https://www.youtube.com/results?search_query=ai+Context+Conditional+Latent+Adversarial+network+CALM YouTube]
 [https://www.quora.com/search?q=ai%20Context%20Conditional%20Latent%20Adversarial%20network%20CALM ... Quora]

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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
 }}
-[https://www.youtube.com/results?search_query=Imitation+Learning+Machine YouTube search...]
+[https://www.youtube.com/results?search_query=ai+Imitation+Learning YouTube]
-[https://www.google.com/search?q=Imitation+Learning+Machine+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.quora.com/search?q=ai%20Imitation%20Learning ... Quora]
 * [[Learning Techniques]]
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-The ongoing explosion of spatiotemporal tracking data has now made it possible to analyze and model fine-grained behaviors in a wide range of domains. For instance, tracking data is now being collected for every NBA basketball game with players, referees, and the ball tracked at 25 Hz, along with annotated game events such as passes, shots, and fouls. Other settings include laboratory animals, people in public spaces, professionals in settings such as operating rooms, actors speaking and performing, digital avatars in virtual environments, and even the behavior of other computational systems.
+Imitation learning (IL) is a type of machine learning in which an agent learns to perform a task by observing and imitating the behavior of an expert. The expert can be a human or another machine. The ongoing explosion of spatiotemporal tracking data has now made it possible to analyze and model fine-grained behaviors in a wide range of domains. For instance, tracking data is now being collected for every NBA basketball game with players, referees, and the ball tracked at 25 Hz, along with annotated game events such as passes, shots, and fouls. Other settings include laboratory animals, people in public spaces, professionals in settings such as operating rooms, actors speaking and performing, digital avatars in virtual environments, and even the behavior of other computational systems. IL is often used in situations where it is difficult or expensive to directly specify the desired behavior in terms of a reward function, such as in robotics or autonomous driving.