Deep reinforcement learning: Difference between revisions

Revision as of 01:57, 9 May 2025

Revision as of 16:54, 10 May 2025

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'''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 [[Autonomousautonomous system|autonomous systems]]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 [[Autonomousautonomous system|autonomous systems]]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 ===

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DRL has been applied to wide range of domains that require sequential decision-making and the ability to learn from high-dimensional input data.

One of the most well-known applications is in [[Video game|games]], where DRL agents have demonstrated performance comparable to or exceeding human-level benchmarks. DeepMind's AlphaGo and AlphaStar, as well as OpenAI Five, are notable examples of DRL systems mastering complex games such as [[Go (game)|Go]], [[StarCraft II]], and [[Dota 2]].<ref>Arulkumaran, K. et al. "A brief survey of deep reinforcement learning." arXiv preprint arXiv:1708.05866 (2017). https://arxiv.org/abs/1708.05866</ref> While these systems have demonstrated high performance in constrained environments, their success often depends on extensive computational resources and may not generalize easily to tasks outside their training domains.

In [[robotics]], DRL has been used to train agents for tasks such as locomotion, manipulation, and navigation in both simulated and real-world environments. By learning directly from sensory input, DRL enables robots to adapt to complex dynamics without relying on hand-crafted control rules.<ref>Li, Yuxi. "Deep Reinforcement Learning: An Overview." arXiv preprint arXiv:1701.07274 (2018). https://arxiv.org/abs/1701.07274</ref>

Other growing areas of application include [[finance]] (e.g., portfolio optimization), [[healthcare]] (e.g., treatment planning and medical decision-making), [[natural language processing]] (e.g., dialogue systems), and [[autonomous vehicles]] (e.g., path planning and control).All of these applications shows how DRL deals with real-world problems like uncertainty, sequential reasoning, and high-dimensional data.<ref>OpenAI et al. "Open-ended learning leads to generally capable agents." arXiv preprint arXiv:2302.06622 (2023). https://arxiv.org/abs/2302.06622</ref>

=== Challenges and Limitations ===

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