Neural Network-based Reinforcement Learning Algorithm

Context:
- It can range from being a Shallow NNet Reinforcement Learning Algorithm to being a Deep NNet Reinforcement Learning Algorithm.
See: DeepMind.

References

(Krakovsky, 2016) ⇒ Marina Krakovsky. (2016). “Reinforcement Renaissance.” In: Communications of the ACM Journal, 59(8). doi:10.1145/2949662
- QUOTE: The two types of learning — reinforcement learning and deep learning through deep neural networks — complement each other beautifully, says Sutton. " Deep learning is the greatest thing since sliced bread, but it quickly becomes limited by the data, " he explains. " If we can use reinforcement learning to automatically generate data, even if the data is more weakly labeled than having humans go in and label everything, there can be much more of it because we can generate it automatically, so these two together really fit well. " Despite the buzz around DeepMind, combining reinforcement learning withneural networks is not new. TD-Gammon, a backgammon-playing program developed by IBM's Gerald Tesauro in 1992, was a neural network that learned to play backgammon through reinforcement learning (the TD in the name stands for Temporal-Difference learning, still a dominant algorithm in reinforcement learning). “Back then, computers were 10,000 times slower per dollar, which meant you couldn't have very deep networks because those are harder to train … “Deep reinforcement learning is just a buzzword for traditional reinforcement learning combined with deeper neural networks, " he says.