8.2.3-reinforcement-learning.md

8.2.3 Reinforcement learning

🌳 Tip 🌳
To refresh your knowledge on deep RL, checkout Spinning Up in Deep RL (OpenAI)

28. [E] Explain the explore vs exploit tradeoff with examples.

29. [E] How would a finite or infinite horizon affect our algorithms?

30. [E] Why do we need the discount term for objective functions?

31. [E] Fill in the empty circles using the minimax algorithm.

    

32. [M] Fill in the alpha and beta values as you traverse the minimax tree from left to right.

    

33. [E] Given a policy, derive the reward function.

34. [M] Pros and cons of on-policy vs. off-policy.

35. [M] What’s the difference between model-based and model-free? Which one is more data-efficient?