Zobrazeno 1 - 10
of 271
pro vyhledávání: '"Taylor, Matthew E."'
Autor:
Przystupa, Michael, Gidel, Gauthier, Taylor, Matthew E., Jagersand, Martin, Piater, Justus, Tosatto, Samuele
As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay people be able to drive multi-degree-of-freedom platforms with low-dimensional controllers. One approach is to use
Externí odkaz:
http://arxiv.org/abs/2410.21406
In recent times, Reinforcement learning (RL) has been widely applied to many challenging tasks. However, in order to perform well, it requires access to a good reward function which is often sparse or manually engineered with scope for error. Introdu
Externí odkaz:
http://arxiv.org/abs/2409.15521
Traditionally, Reinforcement Learning (RL) problems are aimed at optimization of the behavior of an agent. This paper proposes a novel take on RL, which is used to learn the policy of another agent, to allow real-time recognition of that agent's goal
Externí odkaz:
http://arxiv.org/abs/2407.16220
Autor:
Shahroudnejad, Atefeh, Mousavi, Payam, Perepelytsia, Oleksii, Sahir, Staszak, David, Taylor, Matthew E., Bawel, Brent
Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. This framework employs constrained beam search to derive optimal layout
Externí odkaz:
http://arxiv.org/abs/2407.08633
Autor:
Tomar, Manan, Hansen-Estruch, Philippe, Bachman, Philip, Lamb, Alex, Langford, John, Taylor, Matthew E., Levine, Sergey
We introduce a new family of video prediction models designed to support downstream control tasks. We call these models Video Occupancy models (VOCs). VOCs operate in a compact latent space, thus avoiding the need to make predictions about individual
Externí odkaz:
http://arxiv.org/abs/2407.09533
Autor:
Muslimani, Calarina, Grooten, Bram, Mamillapalli, Deepak Ranganatha Sastry, Pechenizkiy, Mykola, Mocanu, Decebal Constantin, Taylor, Matthew E.
For autonomous agents to successfully integrate into human-centered environments, agents should be able to learn from and adapt to humans in their native settings. Preference-based reinforcement learning (PbRL) is a promising approach that learns rew
Externí odkaz:
http://arxiv.org/abs/2406.06495
To create useful reinforcement learning (RL) agents, step zero is to design a suitable reward function that captures the nuances of the task. However, reward engineering can be a difficult and time-consuming process. Instead, human-in-the-loop (HitL)
Externí odkaz:
http://arxiv.org/abs/2405.00746
This paper introduces the problem of learning to place logic blocks in Field-Programmable Gate Arrays (FPGAs) and a learning-based method. In contrast to previous search-based placement algorithms, we instead employ Reinforcement Learning (RL) with t
Externí odkaz:
http://arxiv.org/abs/2404.13061
Autor:
Parisi, Simone, Mohammedalamen, Montaser, Kazemipour, Alireza, Taylor, Matthew E., Bowling, Michael
In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assumption that rewards are always observable is often not applicable in re
Externí odkaz:
http://arxiv.org/abs/2402.06819
Autor:
Kharyal, Chaitanya, Gottipati, Sai Krishna, Sinha, Tanmay Kumar, Das, Srijita, Taylor, Matthew E.
One of the final frontiers in the development of complex human - AI collaborative systems is the ability of AI agents to comprehend the natural language and perform tasks accordingly. However, training efficient Reinforcement Learning (RL) agents gro
Externí odkaz:
http://arxiv.org/abs/2401.02991