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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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