Results 11 to 20 of about 37,169,246 (294)
Continuous-Time Model-Based Reinforcement Learning [PDF]
CoRR, 2021 Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in continuous-time. To avoid time-discretization approximation of the underlying process, we propose a continuous-time MBRL framework based on a novel actor-critic method.
Heinonen Markus +2 more
core +7 more sources
Model-based Reinforcement Learning: A Survey
Foundations and Trends® in Machine Learning, 2023 Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This survey is an integration of both fields, better known as model-based reinforcement learning.
Moerland, T.M. +3 more
openaire +4 more sources
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 +6 more sources
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
Model-Based Reinforcement Learning with SINDy
CoRR, 2022 8 pages, 1 figure, 1 table, 1 algorithm, presented at the Decision Awareness in Reinforcement Learning workshop held at the International Conference on Machine Learning, 22 July 2022, Baltimore MD ...
Rushiv Arora +2 more
openaire +3 more sources
The ubiquity of model-based reinforcement learning [PDF]
Current Opinion in Neurobiology, 2012 The reward prediction error (RPE) theory of dopamine (DA) function has enjoyed great success in the neuroscience of learning and decision-making. This theory is derived from model-free reinforcement learning (RL), in which choices are made simply on the basis of previously realized rewards. Recently, attention has turned to correlates of more flexible,
Bradley B, Doll +2 more
openaire +2 more sources
Learning to Paint With Model-Based Deep Reinforcement Learning [PDF]
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 Accepted to ICCV ...
Zhewei Huang +2 more
openaire +3 more sources
Model-based multi-objective reinforcement learning [PDF]
2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2014 This paper describes a novel multi-objective reinforcement learning algorithm. The proposed algorithm first learns a model of the multi-objective sequential decision making problem, after which this learned model is used by a multi-objective dynamic programming method to compute Pareto op-timal policies.
Marco A. Wiering +2 more
openaire +3 more sources
Model-Based Reinforcement Learning for Atari
CoRR, 2019 Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- substantially more, in fact, than a human would need to learn the same games. How can people learn so quickly?
Lukasz Kaiser +13 more
openaire +5 more sources