Answers.Body: <p>This is taken from Sutton &amp; Barto's RL book</p>
  <pre><code>Algorithm parameters: step size alpha in (0, 1], small epsilon &gt; 0
  Initialize Q(s, a), for all s in S+, action a in A(s), arbitrarily except that Q(terminal, ·)=0
  Loop for each episode:
  Initialize S
  Loop for each step of episode:
  Choose A from S using policy derived from Q (e.g., epsilon-greedy)
  Take action A, observe R, S'
  Q(S, A) = Q(S, A) + alpha[R +  gamma * max(a) Q(S', a)  Q(S, A)]
  S = S'
  until S is terminal
  </code></pre>
  <p>What this means is</p>
  <ol>
  <li>Initialize you alpha between 0 and 1, and an epsilon &gt; 0</li>
  <li>Initialize your Q for all states and actions i.e. if you have a 3x3 world with 4 actions (up, down, left, right) it would be a 3x3x4 array.</li>
  <li>Pick a number of episodes for the robot to move in the world to train for.</li>
  <li>Initialize your state (perhaps 0x0)</li>
  <li>Do these things for each step in the episode</li>
  <li>Choose an action a based on your state s in Q BUT allowing for randomness (exploration) based on your epsilon. Meaning, pick a random number between 0 and 1 and if it is greater than epsilon act randomly, otherwise pick the best/max action from Q.</li>
  <li>Feed the action to the world/environment and record the R/Reward S'/S Prime/next State.</li>
  <li>Update your Q array at Q(S, A) (i.e. Q[0, 0, 1] which would mean we are at 0, 0 and going up) with <code>Q(S, A)</code> (yes, that is the value of where you are) + <code>alpha</code> (usually a small value like .005) <code>* (R/Reward + gamma * (max_aQ(S', A)</code>(the best/max action a of S'/the next state) <code>- Q(S, A)</code>/the current state)</li>
  </ol>
  <p>8 is clearer as a formula if you look at the formula in the algorithm. The point is to slowly increment Q(S, A) using alpha to the true reward value by tying it the the next State via a specific action. When we get to that true value we use our Q array (called exploitation) at any given state and pick the max value from the set of possible actions.</p>
  <ol start="9">
  <li>Update S to the next state until we reach the terminal state (3x3 in my example)</li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-11-08T19:36:34.367Z
Answers.LastActivityDate: 2023-11-08T19:36:34.367Z
Answers.UserDisplayName: A_Arnold
Body: <p>I read this article about Q-learning:
  <a href="https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/" rel="nofollow noreferrer">https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/</a></p>
  <p>It teaches how to implement the algorithm using the Gym Python library.</p>
  <p><strong>But, what are the steps of Q-learning from scratch?</strong></p>
  <p><strong>Please, could someone can explain what are the basic steps of the Q-learning algorithm?</strong></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-11-04T15:44:40.903Z
Comments.Text: Hello @will The J and welcome to AI Stack Exchange. This question as posed is very broad. If possible, it may help to edit the question to ask a specific question about Q-learning. Even if you need to make multiple posts, you are much more likely to get answers by making a few specific questions than one broad question here. Thank you for participating, and we hope to see more of your questions here in the near future!
Comments.UserDisplayName: DeepQZero
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-11-04T16:03:17.173Z
Comments.Text: Thank you very much for wanting to help! Okay, I edited the question to make it more specific.
Comments.UserDisplayName: will The J
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-11-10T01:55:52.783Z
Comments.Text: Any book on RL should contain a description of Q-learning, which is like the most famous RL algorithm ever. Having said that I've written 1 or 2 answers that attempt to explain Q-learning, but I don't remember their title. You can search on the site.
Comments.UserDisplayName: nbro
Tags: <reinforcement-learning><q-learning>
Title: in simple words, what is the Q-learning algortimn steps?
UserDisplayName: will The J
ThreadText: # in simple words, what is the Q-learning algortimn steps?
  
  (Asked by: username_0 on 2023-11-04)
  
  I read this article about Q-learning:
  <https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/>
  
  It teaches how to implement the algorithm using the Gym Python library.
  
  **But, what are the steps of Q-learning from scratch?**
  
  **Please, could someone can explain what are the basic steps of the Q-learning algorithm?**
  
  **Tags:** reinforcement-learning, q-learning
  > **Comments:**
  > **username_1**: Hello @will The J and welcome to AI Stack Exchange. This question as posed is very broad. If possible, it may help to edit the question to ask a specific question about Q-learning. Even if you need to make multiple posts, you are much more likely to get answers by making a few specific questions than one broad question here. Thank you for participating, and we hope to see more of your questions here in the near future!  
  > **username_0**: Thank you very much for wanting to help! Okay, I edited the question to make it more specific.  
  > **username_2**: Any book on RL should contain a description of Q-learning, which is like the most famous RL algorithm ever. Having said that I've written 1 or 2 answers that attempt to explain Q-learning, but I don't remember their title. You can search on the site.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2023-11-08. Score: 3 | Accepted answer
  
  This is taken from Sutton & Barto's RL book
  Algorithm parameters: step size alpha in (0, 1], small epsilon > 0
  Initialize Q(s, a), for all s in S+, action a in A(s), arbitrarily except that Q(terminal, ·)=0
  Loop for each episode:
  Initialize S
  Loop for each step of episode:
  Choose A from S using policy derived from Q (e.g., epsilon-greedy)
  Take action A, observe R, S'
  Q(S, A) = Q(S, A) + alpha[R +  gamma * max(a) Q(S', a)  Q(S, A)]
  S = S'
  until S is terminal
  
  What this means is
  
  Initialize you alpha between 0 and 1, and an epsilon > 0
  Initialize your Q for all states and actions i.e. if you have a 3x3 world with 4 actions (up, down, left, right) it would be a 3x3x4 array.
  Pick a number of episodes for the robot to move in the world to train for.
  Initialize your state (perhaps 0x0)
  Do these things for each step in the episode
  Choose an action a based on your state s in Q BUT allowing for randomness (exploration) based on your epsilon. Meaning, pick a random number between 0 and 1 and if it is greater than epsilon act randomly, otherwise pick the best/max action from Q.
  Feed the action to the world/environment and record the R/Reward S'/S Prime/next State.
  Update your Q array at Q(S, A) (i.e. Q[0, 0, 1] which would mean we are at 0, 0 and going up) with Q(S, A) (yes, that is the value of where you are) + alpha (usually a small value like .005) * (R/Reward + gamma * (max_aQ(S', A)(the best/max action a of S'/the next state) - Q(S, A)/the current state)
  
  8 is clearer as a formula if you look at the formula in the algorithm. The point is to slowly increment Q(S, A) using alpha to the true reward value by tying it the the next State via a specific action. When we get to that true value we use our Q array (called exploitation) at any given state and pick the max value from the set of possible actions.
  
  Update S to the next state until we reach the terminal state (3x3 in my example)
  
  
  ---
  
  