Deep reinforcement learning: Difference between revisions

Revision as of 02:32, 10 August 2025

Revision as of 02:33, 10 August 2025

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Another challenge is sparse or delayed reward problem, where feedback signals are infrequent, which makes it difficult for agents to attribute outcomes to specific decisions. Techniques such as reward shaping and exploration strategies have been developed to address this issue.<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>

DRL systems also tend to be sensitive to hyperparameters and lack robustness across tasks or environments. Models that are trained in simulation fail very often when deployed in the real world due to discrepancies between simulated and real-world dynamics, a problem known as the "reality gap.". Bias and fairness in DRL systems have also emerged as concerns, particularly in domains like healthcare and finance where imbalanced data can lead to unequal outcomes for underrepresented groups.

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.