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--- public:t-720-atai:atai-21:engineering_assignment_1 [2021/09/16 10:45] – [Assignment 1.1: Deep Reinforcement Learning] thorisson
+++ public:t-720-atai:atai-21:engineering_assignment_1 [2024/04/29 13:33] (current) – external edit 127.0.0.1
@@ Line 29: / Line 29: @@
 Run the code:\\
   $ python main.py
+For the first task (Deep-Reinforcement-Learning) you will need to install pytorch. Since this is different depending on which OS you use and whether you have a GPU which supports CUDA (Nvidia GPUs only) you should follow the installation instructions [[https://pytorch.org/get-started/locally/
+|here]].
 Zip Files:\\
 {{:public:t-720-atai:atai-21:assignment_1_rl.zip|Assignment 1 Reinforcement Learning}}\\
+{{:public:t-720-atai:atai-21:assignment_1_rl_new.zip|Assignment 1 Reinforcement Learning Updated env.py file to correctly apply action noise}}\\
 {{:public:t-720-atai:atai-21:assignment_1_hl.zip|Assignment 1 Human Learning}}
@@ Line 43: / Line 47: @@
     - Plot its improvement in performance over time.
   - **Modified Version.** Evaluate the learner’s performance on a modified version of the cart-pole task. For this you should evaluate at least two of the following modifications of the environments and compare them to the results from 1.:
-    - Noise on observation/ action/ and environment dynamics.
+    - Noise on observation and action.
     - Hide each variable once (x, v, theta, omega) and run the setup with only three observables.
     - Introduce extremely high noise on one observable for all four observables (three normal, one noisy variables).
     - Change the task after a certain amount of epochs. Think of at least three different changes, one is given as an example in the code.
     - Change the discreteness of time/ observables increasing or decreasing the variable resolution.
+  - Calculate the average score, median score, maximum score, and standard deviation of each task.
 \\