Answers.Body: <p>It is not clear form your question, how you use your replay buffer. Basically, you have to store all states/reward tuples and train your agent on the entire buffer.</p>
  <p>Moreover, you should give the agent time to explore (all) states of the world. But if you want to speed up training, you can try to implement <a href="https://medium.com/@jonathan_hui/rl-importance-sampling-ebfb28b4a8c6" rel="nofollow noreferrer">importance sampling</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-10-20T15:08:06.627Z
Answers.Text: But are those fluctuations in my example a kind of unnormal thing? Or are they acceptable for someone not very experienced in field of deep learning?
Answers.UserDisplayName: Martin
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-10-20T15:18:33.510Z
Answers.Text: in my experience, fluctuations is common
Answers.UserDisplayName: Aray Karjauv
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Answers.CreationDate: 2020-10-20T12:43:01.897Z
Answers.LastActivityDate: 2020-10-21T10:00:05.280Z
Answers.LastEditDate: 2020-10-21T10:00:05.280Z
Answers.UserDisplayName: Aray Karjauv
Body: <p>Hello :) I'm pretty new to this community, so let me know if I posted anything incorrectly and I'll try to change it.</p>
  <p>I'm working on the project which aim is to create self-driving agent in CARLA. I built a neural network Xception (decaying  ε-greedy). The other parameters are:</p>
  <blockquote>
  <p>EPISODES: 100<br />
  GAMMA: 0.3<br />
  EPSILON_DECAY: 0.9<br />
  MIN_EPSILON: 0.001
  BATCH: 16</p>
  </blockquote>
  <p>Due to the limited computer resources I chose 100 or 300 epochs to train the model, but it generates much fluctuations:
  <a href="https://i.sstatic.net/oY1YY.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/oY1YY.png" alt="enter image description here" /></a>
  <a href="https://i.sstatic.net/fTrcv.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/fTrcv.png" alt="enter image description here" /></a></p>
  <blockquote>
  <p>EPISODES: 100<br />
  GAMMA: 0.7
  EPSILON_DECAY: 0.9<br />
  MIN_EPSILON: 0.001
  BATCH: 16</p>
  </blockquote>
  <p><a href="https://i.sstatic.net/icpZs.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/icpZs.png" alt="enter image description here" /></a>
  Can anyone suggest how can I improve my results? Or it is only the issue of small number of epochs?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-20T10:54:03.550Z
Comments.Text: how come your gamma is 0.3? This seems quite low, and would suggest you care more about immediate returns than future returns (not something that is typically the case in RL).
Comments.UserDisplayName: David
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-20T15:01:07.543Z
Comments.Text: Changing otherwise to gamma = 0.7 didn't help with fluctuations. I'm not sure if gamma is the issue. Maybe it is connected with the small amount of epochs?
Comments.UserDisplayName: Martin
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-20T15:34:58.773Z
Comments.Text: A more common value for gamma would be 0.9 or 0.99. I would try those.
Comments.UserDisplayName: S2673
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-20T15:47:08.667Z
Comments.Text: @S2673 I'm not sure if this would be ok with autonomous vehicle...
Comments.UserDisplayName: Martin
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-20T15:49:33.413Z
Comments.Text: What do you mean? Do you think it would be dangerous if it was willing to crash as long as it got somewhere? I would still try it anyway to see if the agent learns and then you can change gamma or the reward system.
Comments.UserDisplayName: S2673
Tags: <neural-networks><reinforcement-learning><dqn>
Title: Improving DQN with fluctuations
UserDisplayName: Martin
ThreadText: # Improving DQN with fluctuations
  
  (Asked by: username_0 on 2020-10-20)
  
  Hello :) I'm pretty new to this community, so let me know if I posted anything incorrectly and I'll try to change it.
  
  I'm working on the project which aim is to create self-driving agent in CARLA. I built a neural network Xception (decaying ε-greedy). The other parameters are:
  
  > EPISODES: 100  
  > GAMMA: 0.3  
  > EPSILON\_DECAY: 0.9  
  > MIN\_EPSILON: 0.001
  > BATCH: 16
  
  Due to the limited computer resources I chose 100 or 300 epochs to train the model, but it generates much fluctuations:
  [![enter image description here](https://i.sstatic.net/oY1YY.png)](https://i.sstatic.net/oY1YY.png)
  [![enter image description here](https://i.sstatic.net/fTrcv.png)](https://i.sstatic.net/fTrcv.png)
  
  > EPISODES: 100  
  > GAMMA: 0.7
  > EPSILON\_DECAY: 0.9  
  > MIN\_EPSILON: 0.001
  > BATCH: 16
  
  [![enter image description here](https://i.sstatic.net/icpZs.png)](https://i.sstatic.net/icpZs.png)
  Can anyone suggest how can I improve my results? Or it is only the issue of small number of epochs?
  
  **Tags:** neural-networks, reinforcement-learning, dqn
  > **Comments:**
  > **username_1**: how come your gamma is 0.3? This seems quite low, and would suggest you care more about immediate returns than future returns (not something that is typically the case in RL).  
  > **username_0**: Changing otherwise to gamma = 0.7 didn't help with fluctuations. I'm not sure if gamma is the issue. Maybe it is connected with the small amount of epochs?  
  > **username_2**: A more common value for gamma would be 0.9 or 0.99. I would try those.  
  > **username_0**: @S2673 I'm not sure if this would be ok with autonomous vehicle...  
  > **username_2**: What do you mean? Do you think it would be dangerous if it was willing to crash as long as it got somewhere? I would still try it anyway to see if the agent learns and then you can change gamma or the reward system.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2020-10-20. Score: 2
  
  It is not clear form your question, how you use your replay buffer. Basically, you have to store all states/reward tuples and train your agent on the entire buffer.
  
  Moreover, you should give the agent time to explore (all) states of the world. But if you want to speed up training, you can try to implement [importance sampling](https://medium.com/@jonathan_hui/rl-importance-sampling-ebfb28b4a8c6)
  
  > **Comments:**
  > **username_0**: But are those fluctuations in my example a kind of unnormal thing? Or are they acceptable for someone not very experienced in field of deep learning?  
  > **username_3**: in my experience, fluctuations is common  
  
  ---
  
  