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These Julia source files contain a Julia implementation of policy gradient for OpenAI Gym Pong and Bipedal Walker 2D. Pong solution is based on Andrej Karpathy's solution: http://karpathy.github.io/2016/05/31/rl/ Walker2D currenly only learns "not to fall". Needs a better reward function. Walker2D reads the renderPrint.txt file at some interval. renderPrint.txt has three integers. The first integer is 1 for render and 0 not. The second is 1 for print to screen more data and 0 for not and the third integer is 1 for go on policy and 0 for include some random exploration. Dependencies are: using Dates using DelimitedFiles import Gym import Random to run Pong: include("pgPong.jl") main() to run 2DWalker: include("pg2Dwalker.jl") main()
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Policy Gradient reinforcement learning using OpenAI Gym Pong using the Julia language
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