Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Boney, Rinu"'
Autor:
Amirloo, Elmira, Fauconnier, Jean-Philippe, Roesmann, Christoph, Kerl, Christian, Boney, Rinu, Qian, Yusu, Wang, Zirui, Dehghan, Afshin, Yang, Yinfei, Gan, Zhe, Grasch, Peter
Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively underexplored. Similar to language models, MLLMs for im
Externí odkaz:
http://arxiv.org/abs/2407.02477
Reinforcement learning is able to solve complex sequential decision-making tasks but is currently limited by sample efficiency and required computation. To improve sample efficiency, recent work focuses on model-based RL which interleaves model learn
Externí odkaz:
http://arxiv.org/abs/2306.09466
Autor:
Nakhleh, Khaled, Raza, Minahil, Tang, Mack, Andrews, Matthew, Boney, Rinu, Hadzic, Ilija, Lee, Jeongran, Mohajeri, Atefeh, Palyutina, Karina
We study the training performance of ROS local planners based on Reinforcement Learning (RL), and the trajectories they produce on real-world robots. We show that recent enhancements to the Soft Actor Critic (SAC) algorithm such as RAD and DrQ achiev
Externí odkaz:
http://arxiv.org/abs/2303.11801
Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment. However, depending on the quality of the offline dataset, such pre-trained agents may have limited
Externí odkaz:
http://arxiv.org/abs/2210.13846
In many control problems that include vision, optimal controls can be inferred from the location of the objects in the scene. This information can be represented using feature points, which is a list of spatial locations in learned feature maps of an
Externí odkaz:
http://arxiv.org/abs/2106.07995
We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Previous attempts to tackle such challenging games have largely focused on model-free learning methods, often requiring hundre
Externí odkaz:
http://arxiv.org/abs/2012.12186
Current robot platforms available for research are either very expensive or unable to handle the abuse of exploratory controls in reinforcement learning. We develop RealAnt, a minimal low-cost physical version of the popular `Ant' benchmark used in r
Externí odkaz:
http://arxiv.org/abs/2011.03085
We present Learning to Drive (L2D), a low-cost benchmark for real-world reinforcement learning (RL). L2D involves a simple and reproducible experimental setup where an RL agent has to learn to drive a Donkey car around three miniature tracks, given o
Externí odkaz:
http://arxiv.org/abs/2008.00715
Model-based reinforcement learning could enable sample-efficient learning by quickly acquiring rich knowledge about the world and using it to improve behaviour without additional data. Learned dynamics models can be directly used for planning actions
Externí odkaz:
http://arxiv.org/abs/1910.05527
Autor:
Boney, Rinu, Di Palo, Norman, Berglund, Mathias, Ilin, Alexander, Kannala, Juho, Rasmus, Antti, Valpola, Harri
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting inaccuracies of the learned model. We propose to regularize trajectory op
Externí odkaz:
http://arxiv.org/abs/1903.11981