In more technical terms, RL is a technique that allows an agent to interact with an environment by taking actions in order to maximise the total rewards.3<\/sup> Consider this metaphor: A young child is handed the TV\u2019s remote control at your house. The scenario can be broken down as follows: <\/p>\n RL is usually modelled as a Markov Decision Process (MDP)6<\/sup><\/p>\n\n\n Deep learning (DL) belongs in the machine-learning family, where artificial neural networks \u2013 algorithms that work similarly to the human brain \u2013 learn from large data sets.7<\/sup> At its core, AI<\/a> enables machines to carry out tasks that would ordinarily need human intelligence. This includes machine learning<\/a>, of which deep learning is a subset.<\/p>\n\n\n The \u2018deep\u2019 in DL refers to the multiple (deep) layers of neural networks needed to facilitate learning. The DL algorithm repeatedly performs a task, and tweaks it every time to improve the end result, thus eliminating the need for implicit programming.8<\/sup><\/p>\n\n\n\n DL\u2019s primary resource for learning is the vast amount of data that is generated every day \u2013 over 2.5 quintillion bytes of data and climbing \u2013 which gives it the information needed to solve nearly any problem that requires \u2018thought\u2019 to answer.9<\/sup> Coupled with the improved computing power that is available today, DL allows machines to find solutions to problems, regardless of the state of the data being input \u2013 whether unstructured, inter-connected, or very diverse \u2013 it doesn\u2019t matter; the more DL algorithms learn, the better they become at finding solutions.10<\/sup><\/p>\n\n\n\n Deep reinforcement learning (DRL) is the coming together of these two fields: reinforcement learning (RL) and deep learning (DL).11<\/sup> This combination has dramatically broadened the range of complex decision-making tasks that were previously outside of the capability of machines.<\/p>\n\n\n\n DeepMind\u2019s AlphaZero is a perfect example of deep reinforcement learning in action, where AlphaZero \u2013 a single system that essentially taught itself how to play, and master, chess from scratch \u2013 has been officially tested by chess masters, and repeatedly won.12<\/sup><\/p>\n\n\n\n Traditional chess engines, such as Stockfish13<\/sup> and IBM\u2019s Deep Blue,14<\/sup> base their game plan on thousands of rules and scenarios designed by skilled human players, in order to pre-empt every possible scenario. However, AlphaZero\u2019s approach is completely different: discarding the human rules in favour of deep neural networks and algorithms, it starts training for each game through deep reinforcement learning from a position of random play, with no built-in knowledge baring the basic rules of the game, in order to find a solution that will position itself as the strongest player in history for that game.<\/p>\n\n\n\n It begins the game with a random play approach, but learns from wins, losses and draws over time, and then adjusts the parameters of the neural network accordingly. In this way, it begins to choose more advantageous moves as it goes. According to DeepMind, AlphaZero needed just nine hours to learn chess.15<\/sup><\/p>\n\n\n\n Garry Kasparov, former World Chess Champion, says, \u201cI can\u2019t disguise my satisfaction that it plays with a very dynamic style, much like my own!”<\/p>\n\n\n\n\n
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\n <\/picture>\n <\/div>\n\n\nWhat is deep learning?<\/h2>\n\n\n\n
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\n <\/picture>\n <\/div>\n\n\nWhat is deep reinforcement learning?<\/h2>\n\n\n\n
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\n \n Artificial Intelligence Strategy online short course <\/a>\n <\/div>\n <\/div>\n ![\"Read](\"https:\/\/www.getsmarter.com\/blog\/wp-content\/themes\/blog\/assets\/icn_arrow-circle_right.svg\"){kind=icon}
\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n<\/div>\n\n\n\nSuccessful applications of deep reinforcement learning<\/h3>\n\n\n\n