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pro vyhledávání: '"Dazeley, Richard"'
In human-AI coordination scenarios, human agents usually exhibit asymmetric behaviors that are significantly sparse and unpredictable compared to those of AI agents. These characteristics introduce two primary challenges to human-AI coordination: the
Externí odkaz:
http://arxiv.org/abs/2408.07877
In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the massive surge in industrial big data and advancement in sensing and compu
Externí odkaz:
http://arxiv.org/abs/2405.18843
Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives, represented by vector-valued rewards. Widely-used scalar RL methods s
Externí odkaz:
http://arxiv.org/abs/2402.06266
Autor:
Vamplew, Peter, Foale, Cameron, Hayes, Conor F., Mannion, Patrick, Howley, Enda, Dazeley, Richard, Johnson, Scott, Källström, Johan, Ramos, Gabriel, Rădulescu, Roxana, Röpke, Willem, Roijers, Diederik M.
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards. In this paper we extend
Externí odkaz:
http://arxiv.org/abs/2402.02665
One common approach to solve multi-objective reinforcement learning (MORL) problems is to extend conventional Q-learning by using vector Q-values in combination with a utility function. However issues can arise with this approach in the context of st
Externí odkaz:
http://arxiv.org/abs/2401.03163
This research sheds light on the present and future landscape of Engineering Entrepreneurship Education (EEE) by exploring varied approaches and models adopted in Australian universities, evaluating program effectiveness, and offering recommendations
Externí odkaz:
http://arxiv.org/abs/2308.06943
Autor:
Wang, Weijia, Lu, Xuequan, Shao, Di, Liu, Xiao, Dazeley, Richard, Robles-Kelly, Antonio, Pan, Wei
Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures. Also, they usually ignore the contributions of different neighbouring points during normal estimation, which leads to less acc
Externí odkaz:
http://arxiv.org/abs/2305.04007
The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant us
Externí odkaz:
http://arxiv.org/abs/2210.05187
Deep Q-Networks algorithm (DQN) was the first reinforcement learning algorithm using deep neural network to successfully surpass human level performance in a number of Atari learning environments. However, divergent and unstable behaviour have been l
Externí odkaz:
http://arxiv.org/abs/2210.03325
Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in reinforcement learning and robotic scenarios, to better understand the r
Externí odkaz:
http://arxiv.org/abs/2207.03214