CodeBhavya

AI & MACHINE LEARNING PROGRAM • LEVEL 24 — REINFORCEMENT LEARNING

Calculate Discounted Return with Python

Learn calculate discounted return with python with a short, executable Python example.

IntermediateDiscount factorReturn

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
2.75

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-24-discounted-return.py
Open in compiler
rewards=[1,2,3]
gamma=0.5
result=sum(reward*(gamma**step) for step,reward in enumerate(rewards))
print(result)

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

No line selected

EXPECTED OUTPUT FOR THE SAMPLE

2.75
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Initialize the sample values used to calculate discounted return.
  2. Process the data step by step using Discount factor and Return.
  3. Display the result for calculate discounted return and compare it with the documented sample output.

This example of calculate discounted return processes the sample values in a controlled iteration. It demonstrates Discount factor and Return and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(n)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For calculate discounted return, keep the data shape and value types consistent with Discount factor.

Check this

Keep every dependent statement inside the correct indented Python block.

Check this

Verify the final Discount factor and Return 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.