Difference between revisions of "Proximal Policy Optimization (PPO)"
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* [[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]] | ||
* [[Generative AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]] ... [[Microsoft]]'s [[BingAI]] ... [[You]] ...[[Google]]'s [[Bard]] | * [[Generative AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]] ... [[Microsoft]]'s [[BingAI]] ... [[You]] ...[[Google]]'s [[Bard]] | ||
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Revision as of 14:29, 19 March 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- 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)
- Assistants ... Hybrid Assistants ... Agents ... Negotiation
- Natural Language Processing (NLP) ...Generation ...LLM ...Tools & Services
- Generative AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's BingAI ... You ...Google's Bard
