Answers.Body: <p>Your question contains the answer. Use value function approximation. Tabular methods must compute a value for each state. That becomes unfeasible with large state spaces. Function approximators can genererlize, and perform well even without ever having seen every state.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-05-18T15:54:52.387Z
Answers.LastActivityDate: 2022-05-18T15:54:52.387Z
Answers.UserDisplayName: chessprogrammer
Body: <p>I am new in the field of RL. I am trying to use tabular methods, Q-Learning for solving a problem that takes a lot of time for computation, so I would like to know if there are more efficient methods for it.</p>
  <p>Why are tabular methods are not useful in large state spaces? Maybe too many possible combinations? Could Softmax Action Selection be better than epsilon greedy?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-05-20T09:41:40.303Z
Comments.Text: Please, don't change the meaning of your question, once it's answered. "softmax action selection" and "value function approximation" are the same thing, so it seems that you changed your question. If you have another similar question, ask it in a new post!
Comments.UserDisplayName: nbro
Tags: <reinforcement-learning><q-learning><function-approximation><episodic-tasks>
Title: Could Softmax Action Selection be useful to solve an episodic task with more than 100000 possible states and 2000 actions?
UserDisplayName: Aquila
ThreadText: # Could Softmax Action Selection be useful to solve an episodic task with more than 100000 possible states and 2000 actions?
  
  (Asked by: username_0 on 2022-05-18)
  
  I am new in the field of RL. I am trying to use tabular methods, Q-Learning for solving a problem that takes a lot of time for computation, so I would like to know if there are more efficient methods for it.
  
  Why are tabular methods are not useful in large state spaces? Maybe too many possible combinations? Could Softmax Action Selection be better than epsilon greedy?
  
  **Tags:** reinforcement-learning, q-learning, function-approximation, episodic-tasks
  
  > **Comments:**
  > **username_1**: Please, don't change the meaning of your question, once it's answered. "softmax action selection" and "value function approximation" are the same thing, so it seems that you changed your question. If you have another similar question, ask it in a new post!  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2022-05-18. Score: 1 | Accepted answer
  
  Your question contains the answer. Use value function approximation. Tabular methods must compute a value for each state. That becomes unfeasible with large state spaces. Function approximators can genererlize, and perform well even without ever having seen every state.
  
  
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