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Reinforcement learning in robotics is extremely challenging due to many practical issues, including safety, mechanical constraints, and wear and tear. Typically, these issues are not considered in the machine learning literature. One crucial problem in applying reinforcement learning in the real world is Safe Exploration, which requires physical and safety constraints satisfaction throughout the l
MushroomRL is an open-source Python library developed to simplify the process of im- plementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a com- prehensive and exible framework to minimize the effort in implementing and testing novel RL methodologies. The architecture of MushroomRL is built
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption that learning from different tasks, sharing common properties, is helpful to generalize the knowledge of them resulting in a more effective feature extraction compared to learning a single task. Intuitively, the resulting s
We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows framework to learn stable Stochastic Differential Equations. We prove the Lyapunov stability for a class of Stochastic Differential Equations and we propose a learning algorithm to learn them from a set of demonstrated tr
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems, as those emerging in robotics and unmanned autonomous vehicles. To face these complex systems, a hierarchical and multi-scale representation is crucial. This has brought the interest on Hierarchical Deep Reinforcement learning systems. Despite their successful application, Deep Reinforcement Learn
The ability to navigate in an environment is essential to the autonomy of mobile robots and unmanned autonomous vehicles. Informally, path planning computes a collision-free path from a start location to a goal location in a known environment. Computing such paths accounting for the kinematics of the robot is a problem widely addressed in the literature, often focusing on feasibility and optimalit
In the Multiagent Connected Path Planning problem (MCPP), a team of agents moving in a graph-represented environment must plan a set of start-goal joint paths which ensures global connectivity at each time step, under some communication model. The decision version of this problem asking for the existence of a plan that can be executed in at most a given number of steps is claimed to be NP-complete
In many real-world problems stochasticity is a critical issue for the learning process. The sources of stochasticity come from the transition model, the explorative component of the policy or, even worse, from noisy observations of the reward function. For a finite number of samples, traditional Reinforcement Learning (RL) methods provide biased estimates of the action-value function possibly lead
We present a model-free method for solving the Inverse Reinforcement Learning (IRL) problem given a set of trajectories generated by different experts' policies. In many applications, the observed demonstrations are not produced by the same policy. In fact, they may be provided by multiple experts that follow different (but similar) policies or even by the same expert that does not always replicat
Carbon-14 often dominates the effective dose to the public from authorized discharges from Swedish nuclear power plants (NPPs). In contrast to air-borne releases, water-borne discharges of 14C are currently not routinely monitored at Swedish NPPs. We have measured 14C in Fucus spp. (brown algae, used as bioindicators of 14C) in shallow waters at the Swedish west coast from 2020 to 2024. At Ringhal
Robot control through contact is challenging as it requires reasoning over long horizons and discontinuous system dynamics. Highly dynamic tasks such as Air Hockey additionally require agile behavior, making the corresponding optimal control problems intractable for planning in realtime. Learning-based approaches address this issue by shifting computationally expensive reasoning through contacts t
Label-free chemical imaging of living and functioning systems is the holy grail of biochemical research. However, existing techniques often require extensive sample preparation to remove interfering molecules such as water, rendering many molecular imaging techniques unsuitable for in situ structural studies. Here, we examined freshly extracted tissue biopsies and living small vertebrates at submi
Carbon-14 (14C) is an important contributor to the collective effective dose to the public due to releases from nuclear power plants (NPPs). In Sweden, only airborne emissions of 14C from NPPs are currently routinely monitored, and the existing data on waterborne 14C discharges are limited. A recent study of 14C in brown algae (Fucus spp.) in Swedish coastal waters showed higher F14C values collec
This paper presents a search for a new Z′ resonance decaying into a pair of dark quarks which hadronise into dark hadrons before promptly decaying back as Standard Model particles. This analysis is based on proton-proton collision data recorded at s = 13 TeV with the ATLAS detector at the Large Hadron Collider between 2015 and 2018, corresponding to an integrated luminosity of 139 fb−1. After sele
The identification of jets originating from quarks and gluons, often referred to as quark/gluon tagging, plays an important role in various analyses performed at the Large Hadron Collider, as Standard Model measurements and searches for new particles decaying to quarks often rely on suppressing a large gluon-induced background. This paper describes the measurement of the efficiencies of quark/gluo
The Nordic Society for Radiation Protection (NSFS) was founded in 1964 at the initiative of Rolf Sievert. Its task is to activate the exchange of knowledge and experience in the Nordic countries regarding protection against ionizing and non-ionizing radiation, for all kinds of occupational, medical, or public exposures. NSFS has always included members from all five Nordic countries and was a foun
The antioxidant α1-microglobulin (A1M) has been suggested as kidney protector during 177Lu-octreotate treatment. The aim of this work was to evaluate apoptotic-related transcript expression in kidney cortex and medulla following injection of 177Lu-octreotate and/or A1M. Mice were injected with 177Lu-octreotate, A1M, or 177Lu-octreotate + A1M. Control groups received PBS or vehicle solution. Animal
Study region: The Paranapanema River Basin, located in southeastern Brazil, is characterized by a cascade of large hydropower plants regulated by a nationally coordinated dispatch system. This basin is a representative case of reservoir-regulated rivers in the country, where multiple dams interact to supply electricity while reshaping natural flow regimes. Study focus: This study examines hydropow
Background: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy. While methods exist to generate voxel-wise uncertainty maps from DL-based auto-segmentation models, these maps are rarely presented to clinicians. Purpose: This study aimed to evaluate the impact of DL-generated uncertainty maps on experienced radiation oncologists during the manual correction of DL-based
