From what I have seen, any results involving RL almost always take a massive number of simulations to reach a remotely good policy.
Will any form of RL be viable for real-time systems?
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Short answer: Yes, it is.
Reinforcement learning can be considered as a online learning. That is, you can train your model with a single data/reward pairs. As with any online learning algorithm, there are a few things to consider.
The model tends to forget the knowledge gained. To overcome this problem, one can save new data in a circular buffer called history and train the model with a portion of mix of new and old data. This is actually the common way to train an RL model and can be adopted to real-time systems. There are also others techniques to overcome it.
Another problem is that if only one data point is fed to the network, it will be impossible to apply some techniques, such as Batch normalization.