This project requires Python 3.7 and the following Python libraries installed:
For this coding exercise, you will use OpenAI Gym's Taxi-v2 environment to design an algorithm to teach a taxi agent to navigate a small gridworld. The goal is to adapt all that you've learned in the previous lessons to solve a new environment!
Before proceeding, read the description of the environment in subsection 3.1 of this paper.
You can verify that the description in the paper matches the OpenAI Gym environment by peeking at the code here.
In a terminal or command window, navigate to the top-level project directory finding_donors/ (that contains this README) and run one of the following commands:
python main.pyThis will run the file main.py, which finds the best reward average in a 100 episodes window.
OpenAI Gym defines "solving" this task as getting average return of 9.7 over 100 consecutive trials.
While this coding exercise is ungraded, we recommend that you try to attain an average return of at least 9.1 over 100 consecutive trials (best_avg_reward > 9.1).