Zobrazeno 1 - 10
of 791
pro vyhledávání: '"Williams, Henry A."'
Publikováno v:
ISBN: 978-0-6455655-2-2 ISSN: 1448-2053
One of the bottlenecks preventing Deep Reinforcement Learning algorithms (DRL) from real-world applications is how to explore the environment and collect informative transitions efficiently. The present paper describes bounded exploration, a novel ex
Externí odkaz:
http://arxiv.org/abs/2412.06139
Autor:
Cutler, Elizabeth, Xing, Yuning, Cui, Tony, Zhou, Brendan, van Rijnsoever, Koen, Hart, Ben, Valencia, David, Ong, Lee Violet C., Gee, Trevor, Liarokapis, Minas, Williams, Henry
Publikováno v:
Australasian conference on robotics and automation (ACRA 2023)
Reinforcement Learning (RL) training is predominantly conducted in cost-effective and controlled simulation environments. However, the transfer of these trained models to real-world tasks often presents unavoidable challenges. This research explores
Externí odkaz:
http://arxiv.org/abs/2408.14747
Autor:
Valencia, David, Williams, Henry, Xing, Yuning, Gee, Trevor, Liarokapis, Minas, MacDonald, Bruce A.
Reinforcement Learning (RL) has been widely used to solve tasks where the environment consistently provides a dense reward value. However, in real-world scenarios, rewards can often be poorly defined or sparse. Auxiliary signals are indispensable for
Externí odkaz:
http://arxiv.org/abs/2407.21338
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics
Categorical Distributional Reinforcement Learning (CDRL) has demonstrated superior sample efficiency in learning complex tasks compared to conventional Reinforcement Learning (RL) approaches. However, the practical application of CDRL is encumbered b
Externí odkaz:
http://arxiv.org/abs/2405.02576
With the continued introduction of driverless events to Formula:Society of Automotive Engineers (F:SAE) competitions around the world, teams are investigating all aspects of the autonomous vehicle stack. This paper presents the use of Deep Reinforcem
Externí odkaz:
http://arxiv.org/abs/2401.02903
With the rising popularity of autonomous navigation research, Formula Student (FS) events are introducing a Driverless Vehicle (DV) category to their event list. This paper presents the initial investigation into utilising Deep Reinforcement Learning
Externí odkaz:
http://arxiv.org/abs/2308.13088
Autor:
Qureshi, Ans, Smith, David, Gee, Trevor, Nejati, Mahla, Shahabi, Jalil, Lim, JongYoon, Ahn, Ho Seok, McGuinness, Ben, Downes, Catherine, Jangali, Rahul, Black, Kale, Lim, Hin, Duke, Mike, MacDonald, Bruce, Williams, Henry
Aotearoa New Zealand has a strong and growing apple industry but struggles to access workers to complete skilled, seasonal tasks such as thinning. To ensure effective thinning and make informed decisions on a per-tree basis, it is crucial to accurate
Externí odkaz:
http://arxiv.org/abs/2308.07512
Autor:
Kweon, Andy, Hu, Vishnu, Lim, Jong Yoon, Gee, Trevor, Liu, Edmond, Williams, Henry, MacDonald, Bruce A., Nejati, Mahla, Sa, Inkyu, Ahn, Ho Seok
As technology progresses, smart automated systems will serve an increasingly important role in the agricultural industry. Current existing vision systems for yield estimation face difficulties in occlusion and scalability as they utilize a camera sys
Externí odkaz:
http://arxiv.org/abs/2304.06177
Autor:
Xing, Yuning, Pham, Dexter, Williams, Henry, Smith, David, Ahn, Ho Seok, Lim, JongYoon, MacDonald, Bruce A., Nejati, Mahla
Publikováno v:
Proceedings of the Australasian conference on robotics and automation (ACRA 2022)
Smart farming is a growing field as technology advances. Plant characteristics are crucial indicators for monitoring plant growth. Research has been done to estimate characteristics like leaf area index, leaf disease, and plant height. However, few m
Externí odkaz:
http://arxiv.org/abs/2304.03610
Autor:
Qureshi, Ans, Loh, Neville, Kwon, Young Min, Smith, David, Gee, Trevor, Bachelor, Oliver, McCulloch, Josh, Nejati, Mahla, Lim, JongYoon, Green, Richard, Ahn, Ho Seok, MacDonald, Bruce, Williams, Henry
Following a global trend, the lack of reliable access to skilled labour is causing critical issues for the effective management of apple orchards. One of the primary challenges is maintaining skilled human operators capable of making precise fruitlet
Externí odkaz:
http://arxiv.org/abs/2302.09716