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AI & MACHINE LEARNING PROGRAM • LEVEL 24 — REINFORCEMENT LEARNING

Perform a Q-learning Update with Python

Learn perform a q-learning update with python with a short, executable Python example.

IntermediateQ-learningBellman update

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
4.3

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-24-q-learning-update.py
Open in compiler
q=2.0
reward=3
next_max=4
alpha,gamma=0.5,0.9
q += alpha*(reward+gamma*next_max-q)
print(round(q,2))

GUIDED CODE TOUR • NOT LIVE EXECUTION

Study the program line by line

Use the real compiler button above to run and debug with different inputs.

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

4.3
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Initialize the sample values used to perform a q-learning update.
  2. Apply Q-learning and Bellman update to compute the required result.
  3. Display the result for perform a q-learning update and compare it with the documented sample output.

This example of perform a q-learning update computes the result directly from the prepared sample data. It demonstrates Q-learning and Bellman update and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(1)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For perform a q-learning update, keep the data shape and value types consistent with Q-learning.

Check this

Apply Q-learning in the same order shown by the algorithm; changing the order can change the result.

Check this

Verify the final Q-learning and Bellman update result against the sample before trying new data.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.