arXiv:2510.07650v4 Announce Type: replace Abstract: While most reinforcement learning methods today flatten the distribution of future returns to a single scalar value, distributional RL methods exploit the return distribution to provide stronger learning signals and to enable applications in exploration and safe RL. While the predominant method for estimating the return distribution is by modeling it as a categorical distribution over discrete bins or estimating a finite number of quantiles, such approaches leave unanswered questions about the fine-grained structure of the return distribution
This paper represents continued progress in refining core AI/ML techniques, specifically in reinforcement learning, which underpins increasingly sophisticated autonomous systems.
Improving the robustness and understanding of reinforcement learning through distributional methods could accelerate the development of more capable and reliable AI agents and systems.
The focus on fine-grained return distribution rather than flattened single scalar values or discrete bins enables more nuanced and potentially safer AI behaviors, impacting future application designs.
- · AI researchers
- · Reinforcement learning developers
- · Robotics companies
- · Safety-critical AI applications
- · Developers relying solely on simplified RL models
Increased precision in reinforcement learning models for complex tasks.
Improved performance and safety in autonomous systems reliant on reinforcement learning.
Accelerated development of more robust AI agents for real-world deployment across various sectors.
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Read at arXiv cs.LG