Difference between revisions of "Proximal Policy Optimization (PPO)"
m |
m |
||
| Line 11: | Line 11: | ||
[https://www.bing.com/news/search?q=ai+Proximal+Policy+Optimization+PPO&qft=interval%3d%228%22 ...Bing News] | [https://www.bing.com/news/search?q=ai+Proximal+Policy+Optimization+PPO&qft=interval%3d%228%22 ...Bing News] | ||
| − | + | * [[Policy]] ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]] | |
* [https://arxiv.org/abs/1707.06347 Proximal policy optimization algorithms | J. Schulman, F. Wolski, P. Dhariwal, A. Radford & O. Klimov 2017] | * [https://arxiv.org/abs/1707.06347 Proximal policy optimization algorithms | J. Schulman, F. Wolski, P. Dhariwal, A. Radford & O. Klimov 2017] | ||
| − | |||
| − | |||
* [[Deep Reinforcement Learning (DRL)]] | * [[Deep Reinforcement Learning (DRL)]] | ||
* [[Policy Gradient (PG)]] | * [[Policy Gradient (PG)]] | ||
| Line 31: | Line 29: | ||
*** [[Lifelong Latent Actor-Critic (LILAC)]] | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| + | * [[Generative AI]] ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]] ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]] | ||
* [[Assistants]] ... [[Hybrid Assistants]] ... [[Agents]] ... [[Negotiation]] ... [[Hugging_Face#HuggingGPT|HuggingGPT]] ... [[LangChain]] | * [[Assistants]] ... [[Hybrid Assistants]] ... [[Agents]] ... [[Negotiation]] ... [[Hugging_Face#HuggingGPT|HuggingGPT]] ... [[LangChain]] | ||
* [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ...[[Large Language Model (LLM)|LLM]] ...[[Natural Language Tools & Services|Tools & Services]] | * [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ...[[Large Language Model (LLM)|LLM]] ...[[Natural Language Tools & Services|Tools & Services]] | ||
| − | |||
Revision as of 16:24, 16 April 2023
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)
- Policy Gradient (PG)
- 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)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
- Generative AI ... Conversational AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's Bing ... You ...Google's Bard ... Baidu's Ernie
- Assistants ... Hybrid Assistants ... Agents ... Negotiation ... HuggingGPT ... LangChain
- Natural Language Processing (NLP) ...Generation ...LLM ...Tools & Services
