- Stock: 100
- Modèle: 10103747434870
- Poids: 1.00kg
- Dimensions: 0.10cm x 0.10cm x 0.10cm
Shipping: Free Shipping & Customs Tax included are subject to change, please refer to the quote you received.
Delivery: Accurate and precise order tracking. Learn more
Package Safety: Refund for your damaged or lost package. Learn more
Payments: 
Shop with confidence
Inspect item by item, verify, conduct quality checks, and upload authentic photos of the products.
Consolidate and repackage all items to reduce international logistics costs and facilitate international transportation.
Our customer service is always here if you need help. Click here to chat with us or Chat on WhatsApp
1.1. Deterministic Dynamic Programming
1.1.1. Deterministic Problems
1.1.2. The Dynamic Programming Algorithm
1.1.3. Approximation in Value Space
1.2. Stochastic Dynamic Programming
1.3. Examples, Variations, and Simplifications
1.3.1. Deterministic Shortest Path Problems
1.3.2. Discrete Deterministic Optimization
1.3.3. Problems with a Termination State
1.3.4. Forecasts
1.3.5. Problems with Uncontrollable State Components
1.3.6. Partial State Information and Belief States
1.3.7. Linear Quadratic Optimal Control
1.3.8. Systems with Unknown Parameters - Adaptive Control
1.4. Reinforcement Learning and Optimal Control - Some Terminology
1.5. Notes and Sources
2. Approximation in Value Space
2.1. Approximation Approaches in Reinforcement Learning
2.1.1. General Issues of Approximation in Value Space
2.1.2. Off-Line and On-Line Methods
2.1.3. Model-Based Simplification of the Lookahead Minimization
2.1.4. Model-Free off-Line Q-Factor Approximation
2.1.5. Approximation in Policy Space on Top of Approximation in Value Space
2.1.6. When is Approximation in Value Space Effective？
2.2. ltistep Lookahead
2.2.1. ltistep Lookahead and Rolling Horizon
2.2.2. ltistep Lookahead and Deterministic Problems
2.3. Problem Approximation
2.3.1. Enforced Decomposition
2.3.2. Probabilistic Approximation - Certainty Equivalent Control
2.4. Rollout and the Policy Improvement Principle
2.4.1. On-Line Rollout for Deterministic Discrete Optimization
2.4.2. Stochastic Rollout and Monte Carlo Tree Search
2.4.3. Rollout with an Expert
2.5. On-Line Rollout for Deterministic Infinite-Spaces Problems Optimization Heuristics
2.5.1. Model Predictive Control
2.5.2. Target Tubes and the Constrained Controllability Condition
2.5.3. Variants of Model Predictive Control
2.6. Notes and Sources
3. Parametric Approximation
3.1. Approximation Architectures
3.1.1. Linear and Nonlinear Feature-Based Architectures
3.1.2. Training of Linear and Nonlinear Architectures
3.1.3. Incremental Gradient and Newton Methods
3.2. Neural Networks
3.2.1. Training of Neural Networks
3.2.2. ltilayer and Deep Neural Networks
3.3. Sequential Dynamic Programming Approximation
3.4. Q-Factor Parametric Approximation
3.5. Parametric Approximation in Policy Space by Classification
3.6. Notes and Sources
4. Infinite Horizon Dynamic Programming
4.1. An Overview of Infinite Horizon Problems
4.2. Stochastic Shortest Path Problems
4.3. Discounted Problems
4.4. Semi-Markov Discounted Problems
4.5. Asynchronous Distributed Value Iteration
4.6. Policy Iteration
4.6.1. Exact Policy Iteration
4.6.2. Optimistic and ltistep Lookahead Policy Iteration
4.6.3. Policy Iteration for Q-factors
4.7. Notes and Sources
4.8. Appendix: Mathematical Analysis
4.8.1. Proofs for Stochastic Shortest Path Problems
4.8.2. Proofs for Discounted Problems
4.8.3. Convergence of Exact and Optimistic Policy Iteration
5. Infinite Horizon Reinforcement Learning
5.1. Approximation in Value Space - Performance Bounds
5.1.1. Limited Lookahead
5.1.2. Rollout and Approximate Policy Improvement
5.1.3. Approximate Policy Iteration
5.2. Fitted Value Iteration
5.3. Simulation-Based Policy Iteration with Parametric Approximation
5.3.1. Self-Learning and Actor-Critic Methods
5.3.2. Model-Based Variant of a Critic-Only Method
5.3.3. Model-Free Variant of a Critic-Only Method
5.3.4. Implementation Issues of Parametric Policy Iteration
5.3.5. Convergence Issues of Parametric Policy Iteration Oscillations
5.4. Q-Learning
5.4.1. Optimistic Policy Iteration with Parametric Q-Factor Approximation - SARSA and DQN
5.5. Additional Methods - Temporal Differences
……
内容简介
Conseils pour réduire les coûts d'expédition
1)Débarrassez-vous de l'emballage d'origine, par exemple, si vous achetez une paire de chaussures, vous pourriez vous débarrasser de la boîte à chaussures ce qui économisera beaucoup sur le poids volumétrique.
2)Les frais de port sont normalement facturés par tranche de 0,5KG. Donc, si vous achetez un produit qui ne pèse que 0,1KG, vous pourriez envisager d'acheter 2-3 PCS à la fois, ce qui n'ajoutera pas plus de frais de port à votre commande.
Poids Volumétrique
Le poids volumétrique se réfère à la taille globale d'un colis et est mesuré en kilogrammes volumétriques. Le poids volumétrique peut être calculé en multipliant la longueur, la largeur et la hauteur d'un colis (en cm) et en divisant ce chiffre par 5000 (certains transporteurs utilisent un diviseur de 6000/7000, etc).
Articles Prohibés (mais non limités) pour l'Expédition Internationale,
-
Cigarette
-
Alcool
-
Liquide
-
Couteau/Ciseaux
-
Pistolet jouet
-
Graines
-
Animaux vivants
-
Fleurs réelles
Les articles suivants (mais non limités) pourraient seulement être expédiés avec des lignes logistiques spécifiques,
-
Crème
-
Cosmétiques
-
Encas
-
Médicament