'Q-learning vs temporal-difference vs model-based reinforcement learning
I'm in a course called "Intelligent Machines" at the university. We were introduced with 3 methods of reinforced learning, and with those we were given the intuition of when to use them, and I quote:
- Q-Learning - Best when MDP can't be solved.
- Temporal Difference Learning - best when MDP is known or can be learned but can't be solved.
- Model-based - best when MDP can't be learned.
Are there any good examples explaining when to choose one method over the other?
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