Body: <p>Im quite new to ML and wanna solve Gyms Car Racing v2 using Q-Learning with a Q-Table.
  But I am having problems approaching this. Thats why I am hoping someone more advanced in this field could give me some ideas.</p>
  <p>So my action space is discrete: 0 (do nothing), 1 (steer left), 2 (steer right), 3 (gas), 4 (break). And my obervation is a top down frame of the car and its surroundings.</p>
  <p><strong>My modell ideas:</strong></p>
  <ol>
  <li>I guess I need to extraxt useful information from the frames to keep the car on the track. Id like to stack N consecutive frames together to get the velocity of the car, the position of the car relative to the track, the curve angle relative to the car. That would be my guesses.
  This is my biggest problem, how would I get all the information from the frames? This is the first time doing something with image recognition.</li>
  <li>I program a QAgent with a Q Table with my actions as columns and the information I extracted from the frames as my rows. Because these are probably continuous, I need to discretize them.</li>
  <li>After that I update the Q Table in every t step.</li>
  </ol>
  <p>This would be my idea for solving this environment. What do you think about this? Any tips or thoughts from you? I would really appreciat it. =)</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-06-12T12:55:30.270Z
Comments.Text: Try inspecting the source code ([1](https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/envs/box2d/car_dynamics.py) and [2](https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/envs/box2d/car_racing.py)) of the environment, but keep in mind that with a tabular Q-learning approach you can only handle discrete (low-dimensional) quantities and not continuous and high-dimensional ones like images or velocity unless you bin them or similar.
Comments.UserDisplayName: Luca Anzalone
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-06-13T15:25:39.263Z
Comments.Text: Hey man, thank you for the answer.
  What do you mean by bin the velocity? What is this exactly?
Comments.UserDisplayName: user72952
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-06-13T18:32:08.007Z
Comments.Text: The velocity in principle is a continuous quantity, so you can't use its value to index a Q-table. So, if you [bin it](https://numpy.org/doc/stable/reference/generated/numpy.digitize.html), i.e. you discretize it (like done in histograms) you can take the index of the bin (in which the actual velocity would be contained) to actually index the q-table. That's it. Indeed, you need to carefully tune the number of bins.
Comments.UserDisplayName: Luca Anzalone
Tags: <python><image-recognition><gym>
Title: Tips for solving OpenAI/Faramas Gymnasium Car Racing Environment
UserDisplayName: user72952
ThreadText: # Tips for solving OpenAI/Faramas Gymnasium Car Racing Environment
  
  (Asked by: username_0 on 2023-06-12)
  
  Im quite new to ML and wanna solve Gyms Car Racing v2 using Q-Learning with a Q-Table.
  But I am having problems approaching this. Thats why I am hoping someone more advanced in this field could give me some ideas.
  
  So my action space is discrete: 0 (do nothing), 1 (steer left), 2 (steer right), 3 (gas), 4 (break). And my obervation is a top down frame of the car and its surroundings.
  
  **My modell ideas:**
  
  1. I guess I need to extraxt useful information from the frames to keep the car on the track. Id like to stack N consecutive frames together to get the velocity of the car, the position of the car relative to the track, the curve angle relative to the car. That would be my guesses.
     This is my biggest problem, how would I get all the information from the frames? This is the first time doing something with image recognition.
  2. I program a QAgent with a Q Table with my actions as columns and the information I extracted from the frames as my rows. Because these are probably continuous, I need to discretize them.
  3. After that I update the Q Table in every t step.
  
  This would be my idea for solving this environment. What do you think about this? Any tips or thoughts from you? I would really appreciat it. =)
  
  **Tags:** python, image-recognition, gym
  
  > **Comments:**
  > **username_1**: Try inspecting the source code ([1](https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/envs/box2d/car_dynamics.py) and [2](https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/envs/box2d/car_racing.py)) of the environment, but keep in mind that with a tabular Q-learning approach you can only handle discrete (low-dimensional) quantities and not continuous and high-dimensional ones like images or velocity unless you bin them or similar.  
  > **username_0**: Hey man, thank you for the answer. What do you mean by bin the velocity? What is this exactly?  
  > **username_1**: The velocity in principle is a continuous quantity, so you can't use its value to index a Q-table. So, if you [bin it](https://numpy.org/doc/stable/reference/generated/numpy.digitize.html), i.e. you discretize it (like done in histograms) you can take the index of the bin (in which the actual velocity would be contained) to actually index the q-table. That's it. Indeed, you need to carefully tune the number of bins.  
  
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  ## 0 answers
  
  