RL-Coursera
Implementations of Coursera Reinforcement Learning Specialization.
The structure of this specialization:
1. Fundamentals of Reinforcement Learning
Week 2: Markov Decision Processes
- Assignment: K-armed Bandits and Exploration/Exploitation
Week 3: Value Functions & Bellman Equations
- No assignment
Week 4: Dynamic Programming
- Assignment: Optimal Policies with Dynamic Programming
2. Sample-based Learning Methods
Week 2: Monte Carlo Methods for Prediction & Control
- No assignment
Week 3: Temporal Difference Learning Methods for Prediction
Week 4: Temporal Difference Learning Methods for Control
- Assignment: Q-learning and Expected Sarsa
Week 5: Planning, Learning & Actiong
- Assignment: Dyna-Q and Dyna-Q+
3. Predictions and Control with Function Approximation
Week 1: On-policy Prediction with Approximation
- Assignment: Semi-gradient TD(0) with Stage Aggregation
Week 2: Constructing Features for Prediction
- Assignment: Semi-gradient TD with a Neural Network
Week 3: Function Approximation and Control
Week 4: Policy Gradient
4. A Complete Reinforcement Learning System (Capstone)
Lunar Lander Projects
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Assignment: Build the Lunar Lander Agent
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Assignment: Parameter Study