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Everything about Proximal policy optimization
Model-free reinforcement learning algorithm
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when the policy network is very large. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Proximal policy optimizationA 5-minute orientation
- Skim a recent paper: Quantum architecture search via truly proximal policy optimizationInfluential work since 2022
- Pick up a classic book: Re-framing regional developmentThe most-reprinted book on the subject
- Read the landmark paper: Proximal Policy Optimization With Policy FeedbackCited 237 times
- Go sideways: Policy gradient methodA closely related topic
- Go sideways: Reinforcement learning from human feedbackA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Proximal Policy Optimization With Policy Feedback
Proximal policy optimization via enhanced exploration efficiency
Authentic Boundary Proximal Policy Optimization
Implementing action mask in proximal policy optimization (PPO) algorithm
Improving traffic signal control operations using proximal policy optimization
Proximal policy optimization algorithm for dynamic pricing with online reviews
Proximal policy optimization with reward-based prioritization
Improving proximal policy optimization with alpha divergence
🆕 Influential since 2022
Quantum architecture search via truly proximal policy optimization
Optimal Policy Characterization Enhanced Proximal Policy Optimization for Multitask Scheduling in Cloud Computing
Optimizing Autonomous Vehicle Performance Using Improved Proximal Policy Optimization
Proximal Policy Optimization With Advantage Reuse Competition
Off-Policy Proximal Policy Optimization
📚 Books worth reading
- 📕Re-framing regional developmentPhilip Cooke · 2012
System modelling and optimizationIFIP TC7 Conference on System Modelling and Optimization (17th 1995 Prague, Czech Republic) · 1996
IMPACTS OF RURAL ELECTRIFICATION IN RWANDA · 2015
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Policy gradient method Policy gradient methods are a class of reinforcement learning algorithms and a sub-class of policy optimization methods. Unlike value-based methods which…
- Reinforcement learning from human feedback In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves…
- Model-free (reinforcement learning) In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward…
- Reinforcement learning In machine learning and optimal control, reinforcement learning (RL) (also known as approximate dynamic programming) is concerned with how an intelligent…
- Deep reinforcement learning Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem…
- Actor-critic algorithm The actor-critic algorithm (AC) is a family of reinforcement learning (RL) algorithms that combine policy-based RL algorithms such as policy gradient methods…
- Differentially private stochastic gradient descent Differentially private stochastic gradient descent (DP-SGD) is an algorithmic technique for learning and a refined analysis of privacy costs within the…
- Stochastic variance reduction (Stochastic) variance reduction is an algorithmic approach to minimizing functions that can be decomposed into finite sums. By exploiting the finite sum…