@@ -19,13 +19,13 @@ There are **three tasks** defined in `run_main.py` which can be commented out to
## Assignment Requirements
This assignment will have a written component and a programming component.
Clone the mazeworld environment locally and run the code looking at the implemtation of the sample algorithm.
Your task is to implement three other algortihms on this domain.
Clone the mazeworld environment locally and run the code looking at the implementation of the sample algorithm.
Your task is to implement four other algorithms on this domain.
-**(15%)** Implement Value Iteration
-**(15%)** Implement Policy Iteration
-**(15%)** Implement SARSA
-**(15%)** Implement QLearning
-**(40%)** Report : Write a short report on the problem and the results of your three algorithms. The report should be submited on LEARN as a pdf:
-**(40%)** Report : Write a short report on the problem and the results of your four algorithms. The report should be submited on LEARN as a pdf:
- Describing each algorithm you used, define the states, actions, dynamics. Define the mathematical formulation of your algorithm, show the Bellman updates you use.
- Some quantitative analysis of the results, a default plot for comparing all algorithms is given. You can do more plots than this.
- Some qualitative analysis of you observations where one algorithm works well in each case, what you noticed along the way, explain the differences in performance related to the algorithms.
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@@ -35,7 +35,7 @@ Your task is to implement three other algortihms on this domain.
You will also submit your code to LEARN and grading will be carried out using a combination of automated and manual grading.
Your algorithms should follow the pattern of the `RL_brain.py` and `RL_brainsample_PI.py` files.
We will look at your definition and implmentation which should match the description in the document.
We will also automatically run your code on the given domain on the three tasks define in `run_main.py` as well as other maps you have not seen in order to evaluate it.
We will also automatically run your code on the given domain on the three tasks defined in `run_main.py` as well as other maps you have not seen in order to evaluate it.
Part of your grade will come from the overall performance of your algorithm on each domain.
So make sure your code runs with the given unmodified `maze_env` code if we import your class names.
