Results 11 to 20 of about 12,004,986 (327)
Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning [PDF]
International Journal of Production Research, 2021 We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and reinforcement learning (RL). We formulate the scheduling process of JSSP as a sequential decision-making problem with graph representation of the
Junyoung Park +4 more
semanticscholar +1 more source
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal [PDF]
International Conference on Machine Learning, 2023 In recent years, increasing attention has been directed to leveraging pre-trained vision models for motor control. While existing works mainly emphasize the importance of this pre-training phase, the arguably equally important role played by downstream ...
Yingdong Hu +3 more
semanticscholar +1 more source
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles [PDF]
IEEE International Conference on Robotics and Automation, 2021 Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios. However, the poor photorealism and lack of diverse sensor modalities of existing simulation engines remain key hurdles
Alexander Amini +7 more
semanticscholar +1 more source
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning [PDF]
Machine Intelligence Research, 2022 Dialogue policy learning (DPL) is a key component in a task-oriented dialogue (TOD) system. Its goal is to decide the next action of the dialogue system, given the dialogue state at each turn based on a learned dialogue policy. Reinforcement learning (RL)
Wai-Chung Kwan +3 more
semanticscholar +1 more source
Affordance Learning from Play for Sample-Efficient Policy Learning [PDF]
IEEE International Conference on Robotics and Automation, 2022 Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and how the object is used to achieve a goal. To this end, we propose a novel
Jessica Borja-Diaz +5 more
semanticscholar +1 more source
Journal of Numerical Cognition, 2023 For students to advance beyond arithmetic, they must learn how to attend to the structure of math notation. This process can be challenging due to students' left-to-right computing tendencies.
Vy Ngo +3 more
doaj +1 more source
Meta Policy Learning for Cold-Start Conversational Recommendation [PDF]
Web Search and Data Mining, 2022 Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained by reinforcement learning for a population of users.
Zhendong Chu +4 more
semanticscholar +1 more source
Policy Learning for Nonlinear Model Predictive Control With Application to USVs [PDF]
IEEE transactions on industrial electronics (1982. Print), 2022 The unaffordable computation load of nonlinear model predictive control (NMPC) has prevented it from being used in robots with high sampling rates for decades.
Rizhong Wang +4 more
semanticscholar +1 more source
Learning nullspace policies [PDF]
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010 Many everyday tasks performed by people, such as reaching, pointing or drawing, resolve redundant degrees of freedom in the arm in a similar way. In this paper we present a novel method for learning the strategy used to resolve redundancy by exploiting the variability in multiple observations of different tasks. We demonstrate the effectiveness of this
Towell, Chris +2 more
openaire +3 more sources
A Survey of Deep RL and IL for Autonomous Driving Policy Learning [PDF]
IEEE transactions on intelligent transportation systems (Print), 2021 Autonomous driving (AD) agents generate driving policies based on online perception results, which are obtained at multiple levels of abstraction, e.g., behavior planning, motion planning and control.
Zeyu Zhu, Huijing Zhao
semanticscholar +1 more source