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Adding local short description: "Subfield of machine learning", overriding Wikidata description "techniques combining deep learning and reinforcement learning principles to create efficient machine learning algorithms" |
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{{Short description|Subfield of machine learning}}
'''Deep reinforcement learning''' ('''DRL''') is a subfield of [[machine learning]] that combines principles of [[reinforcement learning]] (RL) and [[deep learning]]. It involves training agents to make decisions by interacting with an environment to maximize cumulative rewards, while using [[Artificial neural networks|deep neural networks]] to represent policies, value functions, or environment models. This integration enables DRL systems to process high-dimensional inputs, such as images or continuous control signals, making the approach effective for solving complex tasks. Since the introduction of the [[Q-learning|deep Q-network (DQN)]] in 2015, DRL has achieved significant successes across domains including [[Video game|games]], [[robotics]], and [[autonomous system]]s, and is increasingly applied in areas such as healthcare, finance, and autonomous vehicles.
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