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Gear up for jobs in high-demand fields: data analytics, digital marketing, and more.By the end of this course you will be able to:
- Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience
- Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model
- Understand the connections between Monte Carlo and Dynamic Programming and TD.
- Implement and apply the TD algorithm, for estimating value functions
- Implement and apply Expected Sarsa and Q-learning (two TD methods for control)
- Understand the difference between on-policy and off-policy control
- Understand planning with simulated experience (as opposed to classic planning strategies)
- Implement a model-based approach to RL, called Dyna, which uses simulated experience
- Conduct an empirical study to see the improvements in sample efficiency when using Dyna