March 2026

Reinforcement learning algorithms are typically designed for generic Markov Decision Processes (MDPs), where any state-action pair can lead to an arbitrary transition distribution. In many practical systems, however, only a subset of the state variables is directly influenced by the agent’s actions, while the remaining components evolve according to exogenous dynamics and account for most of the stochasticity. In this work, we study a structured class of MDPs characterized by exogenous state components whose transitions are independent of […]

Ver mais

Reinforcement learning algorithms are typically designed for generic Markov Decision Processes (MDPs), where any state-action pair can lead to an arbitrary transition distribution. In many practical systems, however, only a subset of the state variables is directly influenced by the agent’s actions, while the remaining components evolve according to exogenous dynamics and account for most of the stochasticity. In this work, we study a structured class of MDPs characterized by exogenous state components whose transitions are independent of […]

Ver mais

Byline: K.H. Koehler A new study is challenging how many approach the concept of intelligence, life, and evolution, and it is doing so by moving beyond massive AI models. The current research paper, “To Wake a Stone with Six Birds,” presents a controlled experiment designed to test if life-like behavior can emerge from inert systems. It attempts to show that the properties we tend to associate with life, such as a forward-moving directive, fixing existing damage, and forming […]

Ver mais

The rise of smart manufacturing under Industry 4.0 introduces mass customization and dynamic production, demanding more advanced and flexible scheduling techniques. The flexible job-shop scheduling problem (FJSP) has attracted significant attention due to its complex constraints and strong alignment with real-world production scenarios. Current deep reinforcement learning (DRL)-based approaches to FJSP predominantly employ constructive methods. While effective, they often fall short of reaching (near-)optimal solutions. In contrast, improvement-based methods iteratively explore the neighborhood of initial solutions and are […]

Ver mais

Accurate prediction of the physicochemical properties of molecular mixtures using graph neural networks remains a significant challenge, as it requires simultaneous embedding of intramolecular interactions while accounting for mixture composition (i.e., concentrations and ratios). Existing approaches are ill-equipped to emulate realistic mixture environments, where densely coupled interactions propagate across hierarchical levels – from atoms and functional groups to entire molecules – and where cross-level information exchange is continuously modulated by composition. To bridge the gap between isolated molecules […]

Ver mais

:::info Astounding Stories of Super-Science October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Picture of Dorian Gray – Chapter IV Astounding Stories of Super-Science October 1994: The Picture of Dorian Gray – Chapter IV By Oscar Wilde ::: One afternoon, a month later, Dorian Gray was reclining in a luxurious arm-chair, in the little library of Lord Henry’s house in Mayfair. It was, […]

Ver mais

In imitation learning, robots are supposed to learn from demonstrations of the desired behavior. Most of the work in imitation learning for swarm robotics provides the demonstrations as rollouts of an existing policy. In this work, we provide a framework based on generative adversarial imitation learning that aims to learn collective behaviors from human demonstrations. Our framework is evaluated across six different missions, learning both from manual demonstrations and demonstrations derived from a PPO-trained policy. Results show that […]

Ver mais

If you’ve ever stared at a broken attribution report at 2am wondering which step in your pipeline silently failed, this post is for you. Modern performance advertising runs on a stack of non-deterministic AI systems – real-time bidding models, CTR predictors, multi-touch attribution, all chained together and funneling data into massive warehouses like Snowflake or Redshift. When something goes wrong (and it will), traditional monitoring gives you almost nothing useful. You get a metric that’s off, but no […]

Ver mais