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pro vyhledávání: '"Hayes, Conor F"'
Reinforcement learning (RL) is a valuable tool for the creation of AI systems. However it may be problematic to adequately align RL based on scalar rewards if there are multiple conflicting values or stakeholders to be considered. Over the last decad
Externí odkaz:
http://arxiv.org/abs/2410.11221
Autor:
Nisioti, Eleni, Glanois, Claire, Najarro, Elias, Dai, Andrew, Meyerson, Elliot, Pedersen, Joachim Winther, Teodorescu, Laetitia, Hayes, Conor F., Sudhakaran, Shyam, Risi, Sebastian
Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work we investigate the potential synergies between LLMs and ALife, drawing on
Externí odkaz:
http://arxiv.org/abs/2407.09502
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
Autor:
Röpke, Willem, Hayes, Conor F., Mannion, Patrick, Howley, Enda, Nowé, Ann, Roijers, Diederik M.
For effective decision support in scenarios with conflicting objectives, sets of potentially optimal solutions can be presented to the decision maker. We explore both what policies these sets should contain and how such sets can be computed efficient
Externí odkaz:
http://arxiv.org/abs/2305.05560
In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from a single execution of a policy. In these settings, making decisions based on the average future returns is not suitable. For example, in a
Externí odkaz:
http://arxiv.org/abs/2211.13032
Many real-world problems contain multiple objectives and agents, where a trade-off exists between objectives. Key to solving such problems is to exploit sparse dependency structures that exist between agents. For example, in wind farm control a trade
Externí odkaz:
http://arxiv.org/abs/2207.00368
Autor:
Reymond, Mathieu, Hayes, Conor F., Willem, Lander, Rădulescu, Roxana, Abrams, Steven, Roijers, Diederik M., Howley, Enda, Mannion, Patrick, Hens, Niel, Nowé, Ann, Libin, Pieter
Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strat
Externí odkaz:
http://arxiv.org/abs/2204.05027
Autor:
Reymond, Mathieu, Hayes, Conor F., Willem, Lander, Rădulescu, Roxana, Abrams, Steven, Roijers, Diederik M., Howley, Enda, Mannion, Patrick, Hens, Niel, Nowé, Ann, Libin, Pieter
Publikováno v:
In Expert Systems With Applications 1 September 2024 249 Part C
Autor:
Vamplew, Peter, Smith, Benjamin J., Kallstrom, Johan, Ramos, Gabriel, Radulescu, Roxana, Roijers, Diederik M., Hayes, Conor F., Heintz, Fredrik, Mannion, Patrick, Libin, Pieter J. K., Dazeley, Richard, Foale, Cameron
The recent paper `"Reward is Enough" by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and artificial. We contest the underlying assumption of Silver et al. tha
Externí odkaz:
http://arxiv.org/abs/2112.15422
In many real-world scenarios, the utility of a user is derived from the single execution of a policy. In this case, to apply multi-objective reinforcement learning, the expected utility of the returns must be optimised. Various scenarios exist where
Externí odkaz:
http://arxiv.org/abs/2106.01048