Difference between revisions of "Proximal Policy Optimization (PPO)"
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| + | = Proximal Policy Optimization with Imitation Learning (PPO-IL) = | ||
| + | * [[Imitation Learning}} | ||
| + | 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. | ||
Revision as of 07:33, 13 August 2023
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- 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)
- Generative AI ... Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu
- Assistants ... Personal Companions ... Agents ... Negotiation ... LangChain
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
Proximal Policy Optimization with Imitation Learning (PPO-IL)
- [[Imitation Learning}}
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.
