Results 41 to 50 of about 1,006,072 (233)
Inverse Reinforcement Learning without Reinforcement Learning
, 2023 Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunately, traditional IRL methods suffer from a computational weakness: they require repeatedly solving a hard reinforcement learning (RL) problem as a subroutine. This is counter-
Swamy, Gokul +3 more
openaire +2 more sources
Efficient Hindsight Experience Replay with Transformed Data Augmentation
Journal of Applied Science and Engineering, 2023 Motion control of robots is a high-dimensional, nonlinear control problem that is often difficult to handle using traditional dynamical path planning means.
Jiazheng Sun, Weiguang Li
doaj +1 more source
Parallel model-based and model-free reinforcement learning for card sorting performance
Scientific Reports, 2020 The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility ...
Alexander Steinke +2 more
doaj +1 more source
Stochastic Reinforcement Learning
, 2019 In reinforcement learning episodes, the rewards and punishments are often non-deterministic, and there are invariably stochastic elements governing the underlying situation.
Kuang, Nikki Lijing +2 more
core +1 more source
Implementing Online Reinforcement Learning with Temporal Neural Networks [PDF]
arXiv, 2022 A Temporal Neural Network (TNN) architecture for implementing efficient online reinforcement learning is proposed and studied via simulation. The proposed T-learning system is composed of a frontend TNN that implements online unsupervised clustering and a backend TNN that implements online reinforcement learning.
arxiv
Virtual to Real Reinforcement Learning for Autonomous Driving
, 2017 Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first
Lu, Cewu +3 more
core +1 more source
Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties
Frontiers in Robotics and AI, 2021 Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases.
S. M. Nahid Mahmud +3 more
doaj +1 more source
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning
, 2021 DQN (Deep Q-Network) is a method to perform Q-learning for reinforcement learning using deep neural networks. DQNs require a large buffer and batch processing for an experience replay and rely on a backpropagation based iterative optimization, making ...
Matsutani, Hiroki +2 more
core
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems [PDF]
arXiv, 2023 Reinforcement learning serves as a potent tool for modeling dynamic user interests within recommender systems, garnering increasing research attention of late. However, a significant drawback persists: its poor data efficiency, stemming from its interactive nature.
arxiv
Selection in Scale-Free Small World
, 2005 In this paper we compare the performance characteristics of our selection based learning algorithm for Web crawlers with the characteristics of the reinforcement learning algorithm. The task of the crawlers is to find new information on the Web.
Farkas, Cs., Lorincz, A., Palotai, Zs.
core +1 more source