Tootfinder

Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport
Yusei Naito, Tomohiko Jimbo, Tadashi Odashima, Takamitsu Matsubara
https://arxiv.org/abs/2404.02362

Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport
Multi-robot cooperative transport is crucial in logistics, housekeeping, and disaster response. However, it poses significant challenges in environments where objects of various weights are mixed and the number of robots and objects varies. This paper presents Task-priority Intermediated Hierarchical Distributed Policies (TIHDP), a multi-agent Reinforcement Learning (RL) framework that addresses these challenges through a hierarchical policy structure. TIHDP consists of three layers: task alloc…

Deep Reinforcement Learning: A Convex Optimization Approach
Ather Gattami
https://arxiv.org/abs/2402.19212 https://arxiv.org/pdf/2402…

Deep Reinforcement Learning: A Convex Optimization Approach
In this paper, we consider reinforcement learning of nonlinear systems with continuous state and action spaces. We present an episodic learning algorithm, where we for each episode use convex optimization to find a two-layer neural network approximation of the optimal $Q$-function. The convex optimization approach guarantees that the weights calculated at each episode are optimal, with respect to the given sampled states and actions of the current episode. For stable nonlinear systems, we show …

Novel method for in-situ drift velocity measurement in large volume TPCs: the Geometry Reference Chamber of the NA61/SHINE experiment at CERN
Andras Laszlo, Adam Gera, Gergo Hamar, Botond Palfi, Piotr Podlaski, Brant Rumberger, Dezso Varga
https://arxiv.org/abs/2405.01285

Novel method for in-situ drift velocity measurement in large volume TPCs: the Geometry Reference Chamber of the NA61/SHINE experiment at CERN
This paper presents a novel method for low maintenance, low ambiguity in-situ drift velocity monitoring in large volume Time Projection Chambers (TPCs). The method was developed and deployed for the 40m^3 TPC tracker system of the NA61/SHINE experiment at CERN, which has a one meter of drift length. The method relies on a low-cost multi-wire proportional chamber (MWPC) placed downstream of the TPCs to be monitored. The drift velocity is then determined by matching the reconstructed tracks in th…

FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
Nicolas Mauricio Cuadrado, Roberto Alejandro Gutierrez, Martin Tak\'a\v{c}
https://arxiv.org/abs/2403.18444

FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
The rise in renewable energy is creating new dynamics in the energy grid that promise to create a cleaner and more participative energy grid, where technology plays a crucial part in making the required flexibility to achieve the vision of the next-generation grid. This work presents FRESCO, a framework that aims to ease the implementation of energy markets using a hierarchical control architecture of reinforcement learning agents trained using federated learning. The core concept we are provin…

This https://arxiv.org/abs/2402.15893 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning
Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents the crucial challenge of ensuring safety. Traditional safe RL approaches have predominantly focused on incorporating predefined safety constraints into the policy learning process. However, this reliance on predefined safety constraints poses limitations in dynamic and unpredictable real-world settings where such con…

Der "Stern" mit einem differenzierten Portrait von #Natenom. Großes Thema ist die #motorisierteGewalt und es wird sogar auf die Verkehrsregeln hingewiesen👍
Bei solchen Interviews merkt man wirklich direkt am Anfang, wie die Redakteur:in drauf ist - in diesem Fa…

A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making
Chitra Subramanian, Miao Liu, Naweed Khan, Jonathan Lenchner, Aporva Amarnath, Sarathkrishna Swaminathan, Ryan Riegel, Alexander Gray
https://arxiv.org/abs/2402.13440

A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making
Multi-agent reinforcement learning (MARL) is well-suited for runtime decision-making in optimizing the performance of systems where multiple agents coexist and compete for shared resources. However, applying common deep learning-based MARL solutions to real-world problems suffers from issues of interpretability, sample efficiency, partial observability, etc. To address these challenges, we present an event-driven formulation, where decision-making is handled by distributed co-operative MARL age…

This https://arxiv.org/abs/2402.19212 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Deep Reinforcement Learning: A Convex Optimization Approach
In this paper, we consider reinforcement learning of nonlinear systems with continuous state and action spaces. We present an episodic learning algorithm, where we for each episode use convex optimization to find a two-layer neural network approximation of the optimal $Q$-function. The convex optimization approach guarantees that the weights calculated at each episode are optimal, with respect to the given sampled states and actions of the current episode. For stable nonlinear systems, we show …

This https://arxiv.org/abs/2402.04061 has been replaced.
link: https://scholar.google.com/scholar?q=a

TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments
Autonomous robots exploring unknown environments face a significant challenge: navigating effectively without prior maps and with limited external feedback. This challenge intensifies in sparse reward environments, where traditional exploration techniques often fail. In this paper, we present TopoNav, a novel topological navigation framework that integrates active mapping, hierarchical reinforcement learning, and intrinsic motivation to enable efficient goal-oriented exploration and navigation …

This https://arxiv.org/abs/2203.02511 has been replaced.
link: https://scholar.google.com/scholar?q=a

Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments
Robots often face situations where grasping a goal object is desirable but not feasible due to other present objects preventing the grasp action. We present a deep Reinforcement Learning approach to learn grasping and pushing policies for manipulating a goal object in highly cluttered environments to address this problem. In particular, a dual Reinforcement Learning model approach is proposed, which presents high resilience in handling complicated scenes, reaching an average of 98% task complet…