Problem
Description
Recall that the TD(λ ) estimator for an MDP can be thought of as a weighted combination of the k-step estimators Ek for k ≥ 1.
Consider the MDP described by the following state diagram. (Assume the discount factor is γ = 1.)
Procedure
- Find a value of λ , strictly less than 1, such that the TD estimate for λ equals that of the
TD(1) estimate. Round your answer for λ to three decimal places.
- This HW is designed to help solidify your understanding of the Temporal Difference algorithms and k-step estimators. You will be given the probability to State 1 and a vector of rewards {r0, r1, r2, r3, r4, r5, r6}
- You will be given 10 test cases for which you will return the best lambda value for each. Your answer will be graded to 0.001 precision. You may use any programming language and libraries you wish.
1
Examples
The following examples can be used to verify your calculation is correct.
- Input: probToState=0.81, valueEstimates={0.0,4.0,25.7,0.0,20.1,12.2,0.0}, rewards={7.9,-5.1,2.5,-7.2,9.0,0.0,1.6}, Output: 0.6226326309908364
- Input: probToState=0.22, valueEstimates={0.0,-5.2,0.0,25.4,10.6,9.2,12.3}, rewards={-2.4,0.8,4.0,2.5,8.6,-6.4,6.1}, Output: 0.49567093118984556
- Input: probToState=0.64, valueEstimates={0.0,4.9,7.8,-2.3,25.5,-10.2,-6.5}, rewards={-2.4,9.6,-7.8,0.1,3.4,-2.1,7.9}, Output: 0.20550275877409016
Resources
The concepts explored in this homework are covered by:
- Lectures
- Lesson 3: TD and Friends
- Readings
- Sutton (1988)
2 C
