If you're not sure what to do during the long weekend, try to implement A2C (advantage actor-critic) in TensorFlow 2.0 with this very solid tutorial:http://inoryy.com/post/tensorflow2-deep-reinforcement-learning/ …
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Also check out this take on A3C (asynchronous advantage actor-critic) -- it was written for TF 1.0 + eager, but it's still perfectly valid in the world of TF 2.0:https://medium.com/tensorflow/deep-reinforcement-learning-playing-cartpole-through-asynchronous-advantage-actor-critic-a3c-7eab2eea5296 …
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Do you know of any real life or business use cases for Deep Rein. learning
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Which method of reinforcement learning do you think works best for control problems like inverted pendulum and robotics in general.. Value based like Q learning or policy based like Actor-critic..? Thanks
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