Deep reinforcement learning: Difference between revisions

Revision as of 23:10, 26 May 2025

Revision as of 20:11, 7 June 2025

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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 (Internet)|autonomous system]]s, and is increasingly applied in areas such as healthcare, finance, and autonomous vehicles.

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Deep reinforcement learning (DRL) is part of [[machine learning]], which combines [[reinforcement learning]] (RL) and [[deep learning]]. In DRL, agents learn how decisions are to be made by interacting with environments in order to maximize cumulative rewards, while using [[Artificial neural networks|deep neural networks]] to represent policies, value functions, or models of the environment. This integration enables agents to handle high-dimensional input spaces, such as raw images or continuous control signals, making DRL a widely used approach for addressing complex tasks.<ref name="Li2018">Li, Yuxi. "Deep Reinforcement Learning: An Overview." ''arXiv'' preprint arXiv:1701.07274 (2018). https://arxiv.org/abs/1701.07274</ref>

Since the development of the [[Q-learning|deep Q-network (DQN)]] in 2015, DRL has led to major breakthroughs in domains such as [[Video game|games]], [[robotics]], and [[Autonomous system (Internet)|autonomous system]]s. Research in DRL continues to expand rapidly, with active work on challenges like sample efficiency and robustness, as well as innovations in model-based methods, transformer architectures, and open-ended learning. Applications now range from healthcare and finance to language systems and autonomous vehicles.<ref name="Arul2017">Arulkumaran, Kai, et al. "A brief survey of deep reinforcement learning." ''arXiv'' preprint arXiv:1708.05866 (2017). https://arxiv.org/abs/1708.05866</ref>

=== Background ===