Accurately quantifying tree cover is an important metric for ecosystem monitoring and for assessing progress in restored sites.

The ability to approach the same problem from different angles is a cornerstone of human intelligence that leads to robust solutions and effective adaptation to problem variations. In contrast, current RL methodologies tend to lead to policies that settle on a single solution to a given problem, making them brittle to problem variations. Replicating human flexibility in reinforcement learning agents is the challenge that we explore in this work.

Belgian authorities say they have arrested three people in connection with a plot to attack Prime Minister Bart De Wever and other politicians using drone-mounted explosives. Federal prosecutor Ann Fransen announced the arrests on Thursday and said the group were under investigation for an "attempted terrorist murder and participation in the activities of a terrorist group", according to Belgian public broadcaster RTBF. "There are also indications that the suspects aimed to construct a drone to which a payload could be attached," she added. Fransen did not name their intended targets, but social media posts from senior figures in De Wever's government indicate that he was on the list. "The news of a planned attack targeting Prime Minister Bart De Wever is deeply shocking," wrote Deputy Prime Minister Maxime Prevot in a post on X. "I express my full support to the Prime Minister, his wife, and his family, as well as my gratitude to the security and justice services whose swift action prevented the worst."

Recent progress has demonstrated that recurrent reinforcement learning (RL), which consists of a context encoder based on recurrent neural networks (RNNs) for unobservable state prediction and a multilayer perceptron (MLP) policy for decision making, can mitigate partial observability and serve as a robust baseline for POMDP tasks.