09 Reinforcement Learning II

How not to repeat yourself. We've already found a way, but maybe there's a better place somewhere. Aka exploration vs. exploitation.

After this practice session, the student

• understands how a discount factor (or other MDP parameters) can affect the resulting policy.

• Q/A session
• Discussion of the bonus quiz from the last week
• Exercise: Influence of discount factor to the resulting policy
• Introduction to RL assignment
• Introduction of the bonus quiz for this week

Effect of discount factor on policy. See pdf

• Calculate Q values from training episodes using temporal difference method
• 0.5 points
• submit your solution to BRUTE lab09quiz, deadline in BRUTE
• format: text file, photo of your solution on paper, pdf - what is convenient for you
• solution will be discussed on the next lab
• Students with their family name starting from A to K (included) have to solve and upload subject A , while students with family name from L to Z have to solve and upload subject B.

• Work on the Reinforcement learning assignment.

Reinforcement learning is now a very active area, also supported by rapid progress in deep neural network learning. A few links for further inspiration:

• Table tennis robot player. Starting from imitation, then generalizing through RL.
• Robotics@google. Well, they can afford many learning episodes many iterations
• Pong game. Learning to play the very old computer game with the help of AI-Gym. YT Video