Answers.Body: <p><span class="math-container">$ \gamma $</span> goes up to 1, but cannot be greater than or equal to 1 (this would make the discounted reward infinite).</p>
  
  <p>The discount factor <span class="math-container">$ \gamma $</span>  determines the importance of future rewards. A factor of 0 will make the agent "myopic" (or short-sighted) by only considering current rewards, while a factor approaching 1 will make it strive for a long-term high reward. If the discount factor meets or exceeds 1, the action values may diverge. For <span class="math-container">$ \gamma =1$</span>, without a terminal state, or if the agent never reaches one, all environment histories become infinitely long, and utilities with additive, undiscounted rewards generally become infinite.</p>
  
  <p>Source:<a href="https://en.wikipedia.org/wiki/Q-learning" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Q-learning</a>
  <a href="https://cs.stanford.edu/people/karpathy/reinforcejs/puckworld.html" rel="nofollow noreferrer">https://cs.stanford.edu/people/karpathy/reinforcejs/puckworld.html</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-04-08T19:36:51.053Z
Answers.Text: +1.  Exactly, simply set gamma to 1, or something very close to it.  Even the value of 0.99 generally works fine, and still avoids the terminal state search issue.
Answers.UserDisplayName: Amrinder Arora
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-04-08T06:39:54.933Z
Answers.LastActivityDate: 2019-04-08T06:39:54.933Z
Answers.UserDisplayName: I_Al-thamary
Answers.Body: <p>You can't! As if you have a <span class="math-container">$\gamma$</span> greater than <span class="math-container">$1$</span> the specified sum for the q-learning will diverge! (goes to infinity in the future steps for <span class="math-container">$\gamma^n$</span>). To know more about that please more scrutinize on the specified formula for the q-learning.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-04-07T21:01:52.513Z
Answers.LastActivityDate: 2019-04-07T21:01:52.513Z
Answers.UserDisplayName: OmG
Body: <p>If I have a DQN, and I care A LOT about future rewards (moreso than current rewards), can I set gamma to a number greater than 1? Like 1.1 perhaps?</p>
  
Tags: <q-learning><dqn>
Title: Can gamma be greater than 1 in a DQN?
UserDisplayName: Shamoon
ThreadText: # Can gamma be greater than 1 in a DQN?
  
  (Asked by: username_0 on 2019-04-07)
  
  If I have a DQN, and I care A LOT about future rewards (moreso than current rewards), can I set gamma to a number greater than 1? Like 1.1 perhaps?
  
  **Tags:** q-learning, dqn
  
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  ## 2 answers
  
  ### Answer by username_1 on 2019-04-08. Score: 2
  
  $ \gamma $ goes up to 1, but cannot be greater than or equal to 1 (this would make the discounted reward infinite).
  
  The discount factor $ \gamma $ determines the importance of future rewards. A factor of 0 will make the agent "myopic" (or short-sighted) by only considering current rewards, while a factor approaching 1 will make it strive for a long-term high reward. If the discount factor meets or exceeds 1, the action values may diverge. For $ \gamma =1$, without a terminal state, or if the agent never reaches one, all environment histories become infinitely long, and utilities with additive, undiscounted rewards generally become infinite.
  
  Source:<https://en.wikipedia.org/wiki/Q-learning>
  <https://cs.stanford.edu/people/karpathy/reinforcejs/puckworld.html>
  
  > **Comments:**
  > **username_2**: +1.  Exactly, simply set gamma to 1, or something very close to it.  Even the value of 0.99 generally works fine, and still avoids the terminal state search issue.  
  
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  ### Answer by username_3 on 2019-04-07. Score: 1
  
  You can't! As if you have a $\gamma$ greater than $1$ the specified sum for the q-learning will diverge! (goes to infinity in the future steps for $\gamma^n$). To know more about that please more scrutinize on the specified formula for the q-learning.
  
  
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