Deep reinforcement learning: Difference between revisions

Revision as of 20:11, 7 June 2025

Revision as of 12:58, 11 June 2025

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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.

similarSimilar area of interest is safe and ethical deployment, particularly in high-risk settings like healthcare, autonomous driving, and finance. Researchers are developing frameworks for safer exploration, interpretability, and better alignment with human values.Ensuring that DRL systems promote equitable outcomes remains an ongoing challenge, especially where historical data may under‑represent marginalized populations.

The future of DRL may also involve more integration with other subfields of machine learning, such as unsupervised learning, transfer learning, and large language models, enabling agents that can learn from diverse data modalities and interact more naturally with human users.<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>