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Computer Science
Artificial Intelligence
Deep Learning
Reinforcement Learning
1. Foundations of Reinforcement Learning
2. Mathematical Foundations
3. Markov Decision Processes
4. Dynamic Programming
5. Monte Carlo Methods
6. Temporal-Difference Learning
7. Function Approximation
8. Deep Reinforcement Learning
9. Policy Gradient Methods
10. Advanced Topics
11. Implementation and Practical Considerations
12. Applications and Case Studies
11.
Implementation and Practical Considerations
11.1.
Algorithm Implementation
11.1.1.
Code Structure and Design
11.1.2.
Hyperparameter Tuning
11.1.3.
Debugging RL Algorithms
11.1.4.
Performance Monitoring
11.2.
Computational Considerations
11.2.1.
Scalability Issues
11.2.2.
Parallel and Distributed Training
11.2.3.
Hardware Acceleration
11.2.4.
Memory Management
11.3.
Evaluation and Benchmarking
11.3.1.
Performance Metrics
11.3.2.
Statistical Significance
11.3.3.
Reproducibility
11.3.4.
Standard Benchmarks
11.4.
Reward Engineering
11.4.1.
Reward Function Design
11.4.2.
Reward Shaping
11.4.3.
Sparse Reward Handling
11.4.4.
Multi-Objective Optimization
11.5.
Sample Efficiency
11.5.1.
Data Requirements
11.5.2.
Sample Complexity Analysis
11.5.3.
Improving Sample Efficiency
11.5.4.
Offline Reinforcement Learning
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10. Advanced Topics
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12. Applications and Case Studies