Prerequisite chain
Prerequisites for Model-Based Reinforcement Learning
Topics you need before working through Model-Based Reinforcement Learning. Direct prerequisites are listed first; transitive prerequisites (the chain reachable through them) follow.
Direct prerequisites (2)
- Bellman Equationslayer 2, tier 1
- Markov Decision Processeslayer 2, tier 1
Reachable through the chain (13)
These topics are not directly cited as prerequisites but are reached transitively by following the chain upward. Working through the direct prerequisites pulls these in.
- Convex Optimization Basicslayer 1, tier 1
- Differentiation in Rnlayer 0A, tier 1
- Sets, Functions, and Relationslayer 0A, tier 1
- Basic Logic and Proof Techniqueslayer 0A, tier 2
- Vectors, Matrices, and Linear Mapslayer 0A, tier 1
- Continuity in Rⁿlayer 0A, tier 1
- Metric Spaces, Convergence, and Completenesslayer 0A, tier 1
- Matrix Operations and Propertieslayer 0A, tier 1
- Concentration Inequalitieslayer 1, tier 1
- Common Probability Distributionslayer 0A, tier 1
- Expectation, Variance, Covariance, and Momentslayer 0A, tier 1
- Random Variableslayer 0A, tier 1
- Kolmogorov Probability Axiomslayer 0A, tier 1