January 2026

Pain management in intensive care usually involves complex trade-offs between therapeutic goals and patient safety, since both inadequate and excessive treatment may induce serious sequelae. Reinforcement learning can help address this challenge by learning medication dosing policies from retrospective data. However, prior work on sedation and analgesia has optimized for objectives that do not value patient survival while relying on algorithms unsuitable for imperfect information settings. We investigated the risks of these design choices by implementing a deep […]

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In the post-Dennard era, optimizing embedded systems requires navigating complex trade-offs between energy efficiency and latency. Traditional heuristic tuning is often inefficient in such high-dimensional, non-smooth landscapes. In this work, we propose a Bayesian Optimization framework using Gaussian Processes to automate the search for optimal scheduling configurations on heterogeneous multi-core architectures. We explicitly address the multi-objective nature of the problem by approximating the Pareto Frontier between energy and time. Furthermore, by incorporating Sensitivity Analysis (fANOVA) and comparing different […]

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How are you, hacker? 🪐 What’s happening in tech today, January 30, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Charles I, King of England, was executed in 1649, Adolf Hitler was appointed Chancellor of Germany in 1933, Mahatma Gandhi was assassinated by a Hindu nationalist. Gandhi in 1948, and we present you with these top quality stories. From The MoSCoW Method: Key to Agile Product Management to Will Media Over […]

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Hey Hackers! Picture this: You just started as a writer. You find this platform that you think is the perfect fit for your articles, so you start publishing with them. But as time goes on, the problems start to show themselves: too many ads, too many paywalls, not enough audience reach. You decide to leave this platform and move on to another one; unfortunately, the same issues arise. Then, after much trial and error, you find the perfect […]

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On-policy deep reinforcement learning remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies, often leading to brittle optimization when gradients are noisy and policy updates must be conservative. In this paper, we revisit policy representation as a first-class design choice for on-policy optimization. We study discretized categorical actors that represent each action dimension with a distribution over bins, yielding a policy objective that resembles a cross-entropy loss. Building […]

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I have an intermediate knowledge about ML algorithms and working of LLMs. I have also made projects using regression and classification and Fine tuned LLMs. So my doubt is that can I start learning and RL just by picking up a self car driving project and learn RL while build it. Nerds please tell me or give me a guide and not for a begnner level submitted by /u/Old-Raspberry-3266 [link] [comments]

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Reinforcement learning has become a cornerstone for enhancing the reasoning capabilities of Large Language Models, where group-based approaches such as GRPO have emerged as efficient paradigms that optimize policies by leveraging intra-group performance differences. However, these methods typically rely on absolute numerical rewards, introducing intrinsic limitations. In verifiable tasks, identical group evaluations often result in sparse supervision, while in open-ended scenarios, the score range instability of reward models undermines advantage estimation based on group means. To address these […]

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