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Yapay ZekâÖnerilenAIML-RL-018

Trust Regions and PPO

Limiting how far one update moves the policy: the clipped surrogate objective behind most applied deep RL and RLHF.

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
4 / 5
Önem
4 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

The learner can explain how PPO's clipping limits the policy ratio and predict what happens when the clip range is too large.

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Rota AIML-RL-018
What Is a Model? Rational Agents and Task Environments Features and Targets Agent and Environment Linear Regression I: Prediction and Residuals Return and Discounting Policies Loss and Fitting: Squared Error Markov Decision Processes Objective Functions and Empirical Risk Value Functions Policy Optimization Gradient Descent in Machine Learning Bellman Intuition Monte Carlo Prediction and Control Stochastic and Mini-batch Optimization Temporal-Difference Learning Policy Gradients: REINFORCE Actor-Critic Methods Trust Regions and PPO