Tootfinder

#Verpasstodon
Umgekippter Mond-Lander Odysseus schickt Bilder
Nach der ersten kommerziellen Mondlandung der Raumfahrtgeschichte liegt der Lander Nova C zwar wohl auf der Seite, schickt aber jetzt erste Bilder.

Former Las Vegas Raiders' weapon Hunter Renfrow finds a new home in the NFL https://www.yardbarker.com/nfl/articles/former_las_vegas_raiders_weapon_hunter_renfrow_finds_a_new_home_in_the_nfl/s1_1731…

Former Las Vegas Raiders' weapon Hunter Renfrow finds a new home in the NFL
Two years ago, the Las Vegas Raiders had one of the best wide receiver rooms in the league, but of course, that was before we saw how bad the offense was going to be. As long as Josh McDaniels was in town, the roster on paper did not matter. Regrettably, it wasn't a smooth ride for one of those talented receivers. Hunter Renfrow, a once 1,000-yard receiver, found himself on the fringes. His stats may not paint a pretty picture now, but he was the one left grappling in a struggling offense. Whil…

Erlangen: Die CSU lehnt die Drei-Städte-Tram ab
Die Stadt-Umland-Bahn zwischen Erlangen, Nürnberg und Herzogenaurach ist seit Jahrzehnten angedacht, seit 2016 wird mit Millionen-Aufwand geplant. Im Erlanger Stadtrat hat die CSU nun aber bekräftigt: Sie will die Tram nicht.

Erlangen: Die CSU lehnt die Drei-Städte-Tram ab
Die Stadt-Umland-Bahn zwischen Erlangen, Nürnberg und Herzogenaurach wird seit 2016 detailliert geplant. Die CSU aber will die Tram nicht.

This https://arxiv.org/abs/2404.12079 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Trajectory Planning for Autonomous Vehicle Using Iterative Reward Prediction in Reinforcement Learning
Traditional trajectory planning methods for autonomous vehicles have several limitations. Heuristic and explicit simple rules make trajectory lack generality and complex motion. One of the approaches to resolve the above limitations of traditional trajectory planning methods is trajectory planning using reinforcement learning. However, reinforcement learning suffers from instability of learning and prior works of trajectory planning using reinforcement learning didn't consider the uncertainties…

UCB-driven Utility Function Search for Multi-objective Reinforcement Learning
Yucheng Shi, Alexandros Agapitos, David Lynch, Giorgio Cruciata, Hao Wang, Yayu Yao, Aleksandar Milenovic
https://arxiv.org/abs/2405.00410

UCB-driven Utility Function Search for Multi-objective Reinforcement Learning
In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours that trade-off between multiple, possibly conflicting, objectives. MORL based on decomposition is a family of solution methods that employ a number of utility functions to decompose the multi-objective problem into individual single-objective problems solved simultaneously in order to approximate a Pareto front of policies. We focus on the case of linear utility functions parameterised …

Three best team fits for former Las Vegas Raiders wide receiver Hunter Renfrow in 2024 https://www.yardbarker.com/nfl/articles/three_best_team_fits_for_former_las_vegas_raiders_wide_receiver…

Three best team fits for former Las Vegas Raiders wide receiver Hunter Renfrow in 2024
Two years ago, the Las Vegas Raiders had one of the best wide receiver rooms in the league, but of course, that was before we saw how bad the offense would be. As long as Josh McDaniels was in town, the roster on paper did not matter. Regrettably, it wasn't a smooth ride for one of those talented receivers. Hunter Renfrow, a once 1,000-yard receiver, found himself on the fringes. His stats may not paint a pretty picture now, but he was the one left grappling in a struggling offense. While Davan…

UCB-driven Utility Function Search for Multi-objective Reinforcement Learning
Yucheng Shi, Alexandros Agapitos, David Lynch, Giorgio Cruciata, Hao Wang, Yayu Yao, Aleksandar Milenovic
https://arxiv.org/abs/2405.00410

UCB-driven Utility Function Search for Multi-objective Reinforcement Learning
In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours that trade-off between multiple, possibly conflicting, objectives. MORL based on decomposition is a family of solution methods that employ a number of utility functions to decompose the multi-objective problem into individual single-objective problems solved simultaneously in order to approximate a Pareto front of policies. We focus on the case of linear utility functions parameterised …

Queue-based Eco-Driving at Roundabouts with Reinforcement Learning
Anna-Lena Schlamp, Werner Huber, Stefanie Schmidtner
https://arxiv.org/abs/2405.00625 ht…

Queue-based Eco-Driving at Roundabouts with Reinforcement Learning
We address eco-driving at roundabouts in mixed traffic to enhance traffic flow and traffic efficiency in urban areas. The aim is to proactively optimize speed of automated or non-automated connected vehicles (CVs), ensuring both an efficient approach and smooth entry into roundabouts. We incorporate the traffic situation ahead, i.e. preceding vehicles and waiting queues. Further, we develop two approaches: a rule-based and an Reinforcement Learning (RL) based eco-driving system, with both using…

Queue-based Eco-Driving at Roundabouts with Reinforcement Learning
Anna-Lena Schlamp, Werner Huber, Stefanie Schmidtner
https://arxiv.org/abs/2405.00625 ht…

Queue-based Eco-Driving at Roundabouts with Reinforcement Learning
We address eco-driving at roundabouts in mixed traffic to enhance traffic flow and traffic efficiency in urban areas. The aim is to proactively optimize speed of automated or non-automated connected vehicles (CVs), ensuring both an efficient approach and smooth entry into roundabouts. We incorporate the traffic situation ahead, i.e. preceding vehicles and waiting queues. Further, we develop two approaches: a rule-based and an Reinforcement Learning (RL) based eco-driving system, with both using…

HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
Malte Lehna, Clara Holzh\"uter, Sven Tomforde, Christoph Scholz
https://arxiv.org/abs/2405.00629

HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
With the growth of Renewable Energy (RE) generation, the operation of power grids has become increasingly complex. One solution is automated grid operation, where Deep Reinforcement Learning (DRL) has repeatedly shown significant potential in Learning to Run a Power Network (L2RPN) challenges. However, only individual actions at the substation level have been subjected to topology optimization by most existing DRL algorithms. In contrast, we propose a more holistic approach in this paper by pro…