Asynchronous advantage actor critic methods are a particular variant of asynchronous deep reinforcement learning that takes a totally different approach to breaking correlations in the data we feed to our deep neural network.
Instead of using a replay buffer, we are going to use many independent agents in their own CPU thread, acting on independent environments. Each of these will collect experiences and help to update the global optimizer and a global actor critic agent. We’ll do “transfer learning” to update each of our local actor critics so that each can take advantage of the experience of the others.
If you like to read content, check out the associated blog post at:
https://www.neuralnet.ai/asynchronous-deep-reinforcement-learning/
Code for this video can be found here:
https://github.com/philtabor/Youtube-Code-Repository/blob/master/ReinforcementLearning/PolicyGradient/A3C/pytorch/a3c.py
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