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cs501r_f2018:lab9 [2018/11/19 21:17]
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cs501r_f2018:lab9 [2018/11/19 21:21]
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   * 45% Proper design, creation and debugging of an actor and critic networks   * 45% Proper design, creation and debugging of an actor and critic networks
   * 25% Proper implementation of the PPO loss function and objective on cart-pole ("​CartPole-v0"​)   * 25% Proper implementation of the PPO loss function and objective on cart-pole ("​CartPole-v0"​)
-  * 20% Implementation and demonstrated learning of PPO on another domain of your choice+  * 20% Implementation and demonstrated learning of PPO on another domain of your choice ​(**except** VizDoom)
   * 10% Visualization of policy return as a function of training   * 10% Visualization of policy return as a function of training
  
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 [[https://​github.com/​joshgreaves/​reinforcement-learning|our lab's implementation of PPO]]. ​ NOTE: because this code comes with a complete implementation of running on VizDoom, **you may not use that as your additional test domain.** [[https://​github.com/​joshgreaves/​reinforcement-learning|our lab's implementation of PPO]]. ​ NOTE: because this code comes with a complete implementation of running on VizDoom, **you may not use that as your additional test domain.**
  
 +Here are some [[https://​stackoverflow.com/​questions/​50667565/​how-to-install-vizdoom-using-google-colab|instructions for installing vizdoom on colab]].
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 +----
  
 Here is some code from our reference implementation. ​ Hopefully it will serve as a good outline of what you need to do. Here is some code from our reference implementation. ​ Hopefully it will serve as a good outline of what you need to do.
cs501r_f2018/lab9.txt ยท Last modified: 2021/06/30 23:42 (external edit)