Deep reinforcement learning: Difference between revisions

Revision as of 05:39, 6 May 2025

Revision as of 01:57, 9 May 2025

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== Deep reinforcement learning ==

==== Introduction ====

'''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|autonomous systems]]. 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 ====

Reinforcement learning (RL) is a framework in which agents interact with environments by taking actions and learning from feedback in form of rewards or penalties. Traditional RL methods, such as [[Q-learning]] and policy gradient techniques, rely on tabular representations or linear approximations, which are often not scalable to high-dimensional or continuous input spaces.

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Since then, DRL has evolved to include various architectures and learning strategies, including model-based methods, actor-critic frameworks, and applications in continuous control environments.<ref>Li, Yuxi. "Deep Reinforcement Learning: An Overview." arXiv preprint arXiv:1701.07274 (2018). https://arxiv.org/abs/1701.07274</ref> These developments have significantly expanded the applicability of DRL across domains where traditional RL was limited.

==== Key Algorithms and Methods ====

Several algorithmic approaches form the foundation of deep reinforcement learning, each with different strategies for learning optimal behavior.

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[[File:Reinforcement learning diagram.svg|thumb|center|upright=1.2|Typical agent–environment interaction in reinforcement learning.]]

==== Applications ====

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

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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 ====

DRL has several significant challenges which limit its broader deployment.

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Additionally, concerns about safety, interpretability, and reproducibility have become increasingly important, especially in high-stakes domains such as healthcare or autonomous driving. These issues remain active areas of research in the DRL community.

==== Recent Advances ====

Recent developments in DRL have introduced new architectures and training strategies which aims to improving performance, efficiency, and generalization.

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In addition, research into open-ended learning has led to the creation of  capable agents that are able to solve a range of tasks without task-specific tuning. Similar systems like the ones that are developed by OpenAI show that agents trained in diverse, evolving environments can generalize across new challenges, moving toward more adaptive and flexible intelligence.<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>

==== Future Directions ====

As deep reinforcement learning continues to evolve, researchers are exploring ways to make algorithms more efficient, robust, and generalizable across a wide range of tasks. Improving sample efficiency through model-based learning, enhancing generalization with open-ended training environments, and integrating foundation models are among the current research goals.