Results 11 to 20 of about 6,531,013 (293)
Reinforcement Learning-based Spectrum Sharing for Cognitive Radio [PDF]
, 2011 This thesis investigates how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system.
Jiang, Tao
core +6 more sources
Improving Exploration in Reinforcement Learning through Domain Knowledge and Parameter Analysis [PDF]
, 2010 This thesis presents novel work on how to improve exploration in reinforcement learning using domain knowledge and knowledge-based approaches to reinforcement learning.
Grzes, Marek
core +7 more sources
Inverse Reinforcement Learning without Reinforcement Learning
CoRR, 2023 Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunately, traditional IRL methods suffer from a computational weakness: they require repeatedly solving a hard reinforcement learning (RL) problem as a subroutine. This is counter-
Gokul Swamy 0001 +3 more
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Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade [PDF]
, 2012 In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when ...
Burt, Graeme +3 more
core +4 more sources
The Journal of Neuroscience, 2021 Humans routinely learn the value of actions by updating their expectations based on past outcomes – a process driven by reward prediction errors (RPEs). Importantly, however, implementing a course of action also requires the investment of effort. Recent work has revealed a close link between the neural signals involved in effort exertion and those ...
Huw Jarvis +5 more
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Reinforcement Learning and Physics
Applied Sciences, 2021 Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the ...
José D. Martín-Guerrero, Lucas Lamata
doaj +1 more source
Reinforcement Learning for Bioretrosynthesis [PDF]
ACS Synthetic Biology, 2019 Abstract Metabolic engineering aims to produce chemicals of interest from living organisms, to advance towards greener chemistry. Despite efforts, the research and development process is still long and costly and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the
Koch, Mathilde +2 more
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Sensors, 2023 Reinforcement learning agents that have not been seen during training must be robust in test environments. However, the generalization problem is challenging to solve in reinforcement learning using high-dimensional images as the input. The addition of a
Sanghoon Park +4 more
doaj +1 more source
Reinforcement learning in populations of spiking neurons [PDF]
, 2008 Population coding is widely regarded as a key mechanism for achieving reliable behavioral responses in the face of neuronal variability. But in standard reinforcement learning a flip-side becomes apparent.
Urbanczik, R +3 more
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Learning to reinforcement learn
CoRR, 2016 17 pages, 7 figures, 1 ...
Wang, Jane +8 more
openaire +4 more sources