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1. 1▶Lecture 11, 2024: On-line training, neural networks, and other approximation architecturesReinforcement Learning Course at ASU, Spring, 2021Notes
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2. 2▶Lecture 4, 2024, POMDP, Systems with Changing Parameters, Adaptive Control, Model Predictive ControlReinforcement Learning Course at ASU, Spring, 2021Notes
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3. 3▶Lecture 13, 2021: An overview of the entire course. Discussion. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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4. 4▶Lecture 12, 2021: Aggregation methods and approximation in value space. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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5. 5▶Lecture 11, 2021: Linear programming, policy approximation, policy gradients. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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6. 6▶Lecture 10, 2021: Approximate policy iteration, Q-learning, parallel versions. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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7. 7▶Lecture 9, 2021: Infinite horizon theory and algorithms. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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8. 8▶Lecture 8, 2021: Neural networks, off-line training. ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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9. 9▶Lecture 7, 2021: Constrained forms of rollout, discrete optimization, ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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10. 10▶Lecture 6, 2021: Model Predictive Control, ASU.Reinforcement Learning Course at ASU, Spring, 2021Notes
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