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Also how much memory is your environment taking, is that the cause of the memory issue?
Hi andrej_, I guess if you keep running into memory issue, you can train with GAIL and resume with GAIL with a smaller demo file with GAIL...
Hi smartriver97, what is your use case for such asynchronous method?
Since Soccer environment is a adversarial environment, it is suppose to converge at 0 reward.
Currently we don't, but you should be able to create such environment by yourself.
We used to have a doc that used Anaconda, but later on we tried to unify the installation workflow across Mac, Linux and Windows, so we decided to...
+1 @MrWetsnow , the version of CUDA depends on your tensorflow version.
@celion_unity
@andrewcoh_unity
To understand these details, I would recommend some good RL courses (For example david silver's course here: [MEDIA])
PPO also works with discrete action space and discrete observation space. Currently Q-learning is not implemented in ML-agents
These are on totally different levels of training. To understand entropy you can refer to this article:...
PPO is a on-policy algorithm, you can learn about it more by looking at this paper. https://arxiv.org/abs/1707.06347
Hi AntonOhammar, I think someone had already tried this and made it work. You can refer to here:...
Hi kvlm, I would highly recommend use our newest version of mlagents (v0.14.1), and try out the GAIL feature.
Nice, glad it worked.
Hi @sahandrez, unfortunately we've deprecated our docker solution, and it probably doesn't work for now.
Hi Procuste, All of the .nn models are understood by Barrcuda as long as you can use tensorflow-to-barracuda.py to convert it. And you should be...
Hi ez303, I highly recommend you check our this tutorial:...
For 0.6 the tensorflowsharp plugin is still downloadable here: https://s3.amazonaws.com/unity-ml-agents/0.5/TFSharpPlugin.unitypackage