Tootfinder

Scaling Population-Based Reinforcement Learning with GPU Accelerated Simulation
Asad Ali Shahid, Yashraj Narang, Vincenzo Petrone, Enrico Ferrentino, Ankur Handa, Dieter Fox, Marco Pavone, Loris Roveda
https://arxiv.org/abs/2404.03336

Scaling Population-Based Reinforcement Learning with GPU Accelerated Simulation
In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks like locomotion and dexterous manipulation. However, this comes at the cost of an enormous amount of experience required for training, exacerbated by the sensitivity of learning efficiency and the policy performance to hyperparameter selection, which often requires numerous trials of time-consuming experiments. This work introduces a Population-Based Reinforcement Learning (…

Deep Reinforcement Learning for Traveling Purchaser Problems
Haofeng Yuan, Rongping Zhu, Wanlu Yang, Shiji Song, Keyou You, Yuli Zhang
https://arxiv.org/abs/2404.02476

Deep Reinforcement Learning for Traveling Purchaser Problems
The traveling purchaser problem (TPP) is an important combinatorial optimization problem with broad applications. Due to the coupling between routing and purchasing, existing works on TPPs commonly address route construction and purchase planning simultaneously, which, however, leads to exact methods with high computational cost and heuristics with sophisticated design but limited performance. In sharp contrast, we propose a novel approach based on deep reinforcement learning (DRL), which addre…

This https://arxiv.org/abs/2404.00686 has been replaced.
link: https://scholar.google.com/scholar?q=a

Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning
Accounting for the uncertainty of value functions boosts exploration in Reinforcement Learning (RL). Our work introduces Maximum Mean Discrepancy Q-Learning (MMD-QL) to improve Wasserstein Q-Learning (WQL) for uncertainty propagation during Temporal Difference (TD) updates. MMD-QL uses the MMD barycenter for this purpose, as MMD provides a tighter estimate of closeness between probability measures than the Wasserstein distance. Firstly, we establish that MMD-QL is Probably Approximately Correct…

Very nice critique of temporal representations (or rather, the lack thereof) in standard models of reinforcement learning (RL).
Namboodiri (2022). How do real animals account for the passage of time during
associative learning?
#Neuroscience

A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
https://arxiv.org/abs/2404.03276

A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
The novel Internet of Things (IoT) paradigm is composed of a growing number of heterogeneous smart objects and services that are transforming architectures and applications, increasing systems' complexity, and the need for reliability and autonomy. In this context, both smart objects and services are often provided by third parties which do not give full transparency regarding the security and privacy of the features offered. Although machine-based Service Level Agreements (SLA) have been recen…

Scaling Population-Based Reinforcement Learning with GPU Accelerated Simulation
Asad Ali Shahid, Yashraj Narang, Vincenzo Petrone, Enrico Ferrentino, Ankur Handa, Dieter Fox, Marco Pavone, Loris Roveda
https://arxiv.org/abs/2404.03336

Scaling Population-Based Reinforcement Learning with GPU Accelerated Simulation
In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks like locomotion and dexterous manipulation. However, this comes at the cost of an enormous amount of experience required for training, exacerbated by the sensitivity of learning efficiency and the policy performance to hyperparameter selection, which often requires numerous trials of time-consuming experiments. This work introduces a Population-Based Reinforcement Learning (…

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…

Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
Ruichen Xu, Xiao Liu, Jinming Xu, Yuan Lin
https://arxiv.org/abs/2403.00446

Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perform safe and appropriate lane change is a popular topic of research in the field of autonomous driving. Currently, few papers consider the safety of reinforcement learning in autonomous lane-change scenarios. We introduce safe hybrid-action reinforcem…

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. For example, heuristic and explicit simple rules limit generalizability and hinder complex motions. These limitations can be addressed using reinforcement learning-based trajectory planning. However, reinforcement learning suffers from unstable learning and existing reinforcement learning-based trajectory planning methods do not consider the uncertainties. Thus, this paper, proposes a reinforcement learnin…

A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
Ahmed Abouelazm, Jonas Michel, J. Marius Zoellner
https://arxiv.org/abs/2405.01440

A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives and guide the agent toward the optimal policy. Since autonomous driving is a complex domain with partly conflicting objectives with varying degrees of priority, developing a suitable reward function represents a fundamental challenge. This paper aims to highlight the gap in such function design by assessing different pr…