10 Reinforcement Learning III

How does Q-learning work for environments with continuous states/actions? Beyond the course contents.

After this practice session, the student

• knows about linear regression as a possible tool to model V- and Q-functions;
• understands in principle how approximative Q-learning works.

• Q/A
• Discussion of the bonus quiz from the last week
• Exercise 1: Approximation minimizing least squares error (LSQ)
• Exercise 2: Approximative Q-learning
• Introduction of the bonus quiz for this week

• Approximation minimizing least squares error (LSQ)
• Approximative Q-learning
• (see pdf)

• Calculate state values during a random walk policy
• 0.5 points
• submit your solution to BRUTE lab10quiz, 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.