Behandelt die Einführung und grundlegende Konzepte des Reinforcement Learning:
- Marov Theorem (Decisions Process, Chains)
- Lernprinzip (Agent, Reward Goal, Environment)
- Dynamic Programming
- Monte Carlo Methods
- Off-policies / On-policies
- Temporal Difference Learning (Q-Learning / SARSA)
- n-Step Temporal Difference
- Function Approximation
- Deep Q-Learning
- Trainer/in: Eric Dolch