Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Ding, Yuhao"'
Optimal control problems can be solved via a one-shot (single) optimization or a sequence of optimization using dynamic programming (DP). However, the computation of their global optima often faces NP-hardness, and thus only locally optimal solutions
Externí odkaz:
http://arxiv.org/abs/2409.00655
Autor:
Gu, Shangding, Shi, Laixi, Ding, Yuhao, Knoll, Alois, Spanos, Costas, Wierman, Adam, Jin, Ming
Safe reinforcement learning (RL) is crucial for deploying RL agents in real-world applications, as it aims to maximize long-term rewards while satisfying safety constraints. However, safe RL often suffers from sample inefficiency, requiring extensive
Externí odkaz:
http://arxiv.org/abs/2405.20860
Publikováno v:
ICLR 2023
Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience. However, the aspect of constraint violations has not been adequately addressed in the existing works, making their applic
Externí odkaz:
http://arxiv.org/abs/2405.16601
In numerous reinforcement learning (RL) problems involving safety-critical systems, a key challenge lies in balancing multiple objectives while simultaneously meeting all stringent safety constraints. To tackle this issue, we propose a primal-based f
Externí odkaz:
http://arxiv.org/abs/2405.16390
Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications. Nevertheless, managing the trade-off between reward and safety during exploration presents a significant challenge. Improving reward performa
Externí odkaz:
http://arxiv.org/abs/2405.01677
Event-based vision represents a paradigm shift in how vision information is captured and processed. By only responding to dynamic intensity changes in the scene, event-based sensing produces far less data than conventional frame-based cameras, promis
Externí odkaz:
http://arxiv.org/abs/2401.05626
We first raise and tackle a ``time synchronization'' issue between the agent and the environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real-world applications. In reality, environmental changes occur over wall-
Externí odkaz:
http://arxiv.org/abs/2309.14989
We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraints. The objective and constraints are described by {\it general utiliti
Externí odkaz:
http://arxiv.org/abs/2305.17568
Autor:
Zhou, Jiajun, Wu, Jiajun, Gao, Yizhao, Ding, Yuhao, Tao, Chaofan, Li, Boyu, Tu, Fengbin, Cheng, Kwang-Ting, So, Hayden Kwok-Hay, Wong, Ngai
To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched. A prominent challenge is to quantize the DNN models into low-bitwidth numbers without significant accuracy degradation, especia
Externí odkaz:
http://arxiv.org/abs/2302.12510
We study the scalable multi-agent reinforcement learning (MARL) with general utilities, defined as nonlinear functions of the team's long-term state-action occupancy measure. The objective is to find a localized policy that maximizes the average of t
Externí odkaz:
http://arxiv.org/abs/2302.07938