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content/research/deepreinforcementlearning/WeeklyProgress/Apr2.md

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+# Progress Report for Week of April 2nd
+
+## Added Video Recording Capability to MinAtar environment
+
+You can now use the OpenAI Monitor Wrapper to watch the actions performed by agents in the MinAtar suite. (Currently the videos are in grayscale)
+
+Problems I had to solve:
+
+- How to represent the channels into a grayscale value
+- Getting the tensor into the right format (with shape and dtype)
+- Adding additional meta information that OpenAI expected
+
+## Progress Towards \#Exploration
+
+After getting nowhere trying to combine the paper on Random Network Distillation and Count-based exploration and Intrinsic Motivation, I turned the paper \#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. 
+
+This paper uses the idea of an autoencoder to learn a smaller latent state representation of the input. We can then use this smaller representation as a hash and count states based on these hashes. 
+
+Playing around with the ideas of autoencoders, I wanted a way to discretized my hash more than just what floating point precision allows. Of course this turns it into a non-differential function which I then tried turning towards Evolutionary methods to solve. Sadly the rate of optimization was drastically diminished using the Evolutionary approach. Therefore, my experiments for this week failed. 
+
+I'll probably look towards implementing what the paper did for my library and move on to a different piece.
+
+
+
+Guru Indian: 3140 Cowan Blvd, Fredericksburg, VA 22401