Deep reinforcement learning: Difference between revisions

Revision as of 23:10, 26 May 2025

Revision as of 23:10, 26 May 2025

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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 Algorithmsalgorithms and Methodsmethods ===

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

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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 Limitationslimitations ===

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 Advancesadvances ===

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 Directionsdirections ===

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.