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The ability to tackle a wide range of Deep RL techniques has been demonstrated to a variety of issues which were previously unsolved. A few of the most renowned accomplishments are in the game of backgammon, beating previous computer programmes, achieving superhuman-level performance from the pixels in Atari games, mastering the game of Go and beating professional poker players in the Nolimit Texas Hold’em Heads Up Game: Libratus and Deep stack.

Such achievements in popular games are essential because in a variety of large and nuanced tasks that require operating with high-dimensional inputs, they explore the effectiveness of deep RL. Deep RL has also shown a great deal of potential for real-world applications such as robotics, self-driving vehicles, finance, intelligent grids, dialogue systems, etc. Deep RL systems are still in production environments, currently. How Facebook uses Deep RL, for instance, can be found for pushing notifications and for faster video loading with smart prefetching.

RL is also relevant to fields where one might assume that supervised learning alone, such as sequence prediction, is adequate. It has also been cast as an RL problem to build the right neural architecture for supervised learning tasks. Notice that evolutionary techniques can also be addressed for certain types of tasks. Finally, it should be remembered that deep RL has prospects in the areas of computer science in classical and basic algorithmic issues, such as the travelling salesman problem. This is an NP-complete issue and the ability to solve it with deep RL illustrates the potential effect it could have on many other NP-complete issues, given that it is possible to manipulate the structure of these problems [2, 12].