reinforcement-learning-algorithms
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Hi, I am trying to use the PPO algorithm; however, it's not clear how to construct the stochastic policy. Should I use the Gaussian policy network?
Cool library by the way; I like the modularity!
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Some time around ae030395f56efca50a51335fe4f3367caf950066 we regressed and the example code in gym_client.cpp doesn't converge any more. Presumably because of some difference in our observation normalization compared to the OpenAI Baselines one.
I'll look in more detail this weekend and confirm if it's that exact commit causing the problem.
Should we have a list of packages that implement models (e.g. https://github.com/JuliaPOMDP/RockSample.jl) on the README? Or should they be listed in another place?
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brtdp_example.py calls def run_sample_trial(self, verbose=True): in BoundedRTDPClass.py,
while not state.is_terminal(): seems in a dead loop
s: (1,1) Action: left Gap: 99.07540345871926 MaxDiff: 9.908648275251627
s: (1,1) Action: left Gap: 99.07540345871926 MaxDiff: 9.908648275251627
s: (1,1) Action: left Gap: 99.07540345871926 MaxDiff: 9.90864827525
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Documentation
Hi,
Do you can add some documentation to your code? Or a HowTo? I was running your code but dont see that you save the learned model right? Because i would like to test it.
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The OpenAI Gym installation instructions are missing reference to the "Build Tools for Visual Studio 2019" from the following site.
https://visualstudio.microsoft.com/downloads/
I also found this by reading the following article.
https://towardsdatascience.com/how-to-install-openai-gym-in-a-windows-environment-338969e24d30
Even though this is an issue in the OpenAI gym, a note in this RE