Difference between revisions of "Proximal Policy Optimization (PPO)"
m (→Proximal Policy Optimization with Imitation Learning (PPO-IL)) |
m |
||
| (5 intermediate revisions by the same user not shown) | |||
| Line 4: | Line 4: | ||
|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, 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 | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
}} | }} | ||
[https://www.youtube.com/results?search_query=ai+Proximal+Policy+Optimization+PPO YouTube] | [https://www.youtube.com/results?search_query=ai+Proximal+Policy+Optimization+PPO YouTube] | ||
| Line 37: | Line 28: | ||
*** [[Lifelong Latent Actor-Critic (LILAC)]] | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| − | * [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[ | + | * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]] |
| − | + | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Apple| Siri | Apple]] ... [[Meta]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Grok]] | [https://x.ai/ xAI] ... [[Groq]] ... [[Ernie]] | [[Baidu]] ... [[DeepSeek]] ... [[Alibaba]] | |
| − | * [[Large Language Model (LLM)]] ... [[ | + | * [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]] |
| + | * [[Agents/Assistants]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]] | ||
| Line 62: | Line 54: | ||
* PPO-IL requires expert demonstrations, which may not be available in all cases. | * PPO-IL requires expert demonstrations, which may not be available in all cases. | ||
| − | * | + | * The expert demonstrations must be of high quality, otherwise the PPO-IL algorithm may learn to perform the task incorrectly. |
* PPO-IL can be more computationally expensive than PPO alone. | * PPO-IL can be more computationally expensive than PPO alone. | ||
| − | |||
| − | |||
Latest revision as of 23:25, 15 September 2026
YouTube ... Quora ...Google search ...Google News ...Bing News
- Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)
- Proximal policy optimization algorithms | J. Schulman, F. Wolski, P. Dhariwal, A. Radford & O. Klimov 2017
- Deep Reinforcement Learning (DRL)
- Reinforcement Learning (RL):
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- Q Learning
- State-Action-Reward-State-Action (SARSA)
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Deep Q Network (DQN)
- Symbiotic Intelligence ... Bio-inspired Computing ... Neuroscience ... Connecting Brains ... Nanobots ... Molecular ... Neuromorphic ... Evolutionary/Genetic
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
- Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
- Conversational AI ... ChatGPT | OpenAI ... Gemini | Google ... Claude | Anthropic ... Bing/Copilot | Microsoft ... Siri | Apple ... Meta ... Perplexity ... You ... phind ... Grok | xAI ... Groq ... Ernie | Baidu ... DeepSeek ... Alibaba
- Large Language Model (LLM) ... Multimodal ... Foundation Models (FM) ... Generative Pre-trained ... Transformer ... Attention ... GAN ... BERT
- Agents/Assistants ... Robotic Process Automation ... Personal Companions ... Productivity ... Email ... Negotiation ... LangChain
Proximal Policy Optimization with Imitation Learning (PPO-IL)
a Reinforcement Learning (RL) 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. PPO-IL combines the strengths of Proximal Policy Optimization (PPO) and Imitation Learning (IL). PPO is a policy gradient algorithm that is known for its stability and sample efficiency, while Imitation Learning (IL)|IL]] is a supervised learning algorithm that can learn from expert demonstrations. PPO-IL works by first collecting a set of expert demonstrations. These demonstrations are then used to train a policy that imitates the behavior of the expert. Once the policy is trained, it can be used to interact with the environment and learn to perform the task at hand. PPO-IL has been shown to be effective in a variety of tasks, including playing Atari games, controlling robots, and driving cars. It is a promising approach for reinforcement learning problems where expert demonstrations are available.
Here are some of the advantages of PPO-IL:
- PPO-IL is more sample efficient than PPO alone, as it can learn from expert demonstrations.
- PPO-IL is more robust to noise and outliers in the expert demonstrations.
- PPO-IL can learn to perform tasks that are difficult or impossible to learn from scratch using PPO alone.
Here are some of the disadvantages of PPO-IL:
- PPO-IL requires expert demonstrations, which may not be available in all cases.
- The expert demonstrations must be of high quality, otherwise the PPO-IL algorithm may learn to perform the task incorrectly.
- PPO-IL can be more computationally expensive than PPO alone.
