Results 31 to 40 of about 6,944,236 (307)
Quantum Enhancements for Deep Reinforcement Learning in Large Spaces
PRX Quantum, 2021 Quantum algorithms have been successfully applied to provide computational speed ups to various machine-learning tasks and methods. A notable exception to this has been deep reinforcement learning (RL).
Sofiene Jerbi +4 more
doaj +1 more source
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
CoRR, 2016 Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge this gap.
Yan Duan +5 more
openaire +2 more sources
Proceedings of the 24th International Conference on Intelligent User Interfaces, 2019 The Keystroke-Level Model (KLM) is a popular model for predicting users' task completion times with graphical user interfaces. KLM predicts task completion times as a linear function of elementary operators. However, the policy, or the assumed sequence of the operators that the user executes, needs to be prespeciffed by the analyst.
Oulasvirta, Antti +3 more
openaire +4 more sources
Reinforcement Learning in Game Industry—Review, Prospects and Challenges
Applied Sciences, 2023 This article focuses on the recent advances in the field of reinforcement learning (RL) as well as the present state–of–the–art applications in games.
Konstantinos Souchleris +2 more
doaj +1 more source
Distentangling the systems contributing to changes in learning during adolescence
Developmental Cognitive Neuroscience, 2020 Multiple neurocognitive systems contribute simultaneously to learning. For example, dopamine and basal ganglia (BG) systems are thought to support reinforcement learning (RL) by incrementally updating the value of choices, while the prefrontal cortex ...
Sarah L. Master +5 more
doaj +1 more source
A brief survey of deep reinforcement learning [PDF]
, 2017 Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world.
Arulkumaran, K +9 more
core +1 more source
Federated Reinforcement Learning Acceleration Method for Precise Control of Multiple Devices
IEEE Access, 2021 Nowadays, Reinforcement Learning (RL) is applied to various real-world tasks and attracts much attention in the fields of games, robotics, and autonomous driving.
Hyun-Kyo Lim +4 more
doaj +1 more source
Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
CoRR, 2019 Hyperparameter tuning is an omnipresent problem in machine learning as it is an integral aspect of obtaining the state-of-the-art performance for any model. Most often, hyperparameters are optimized just by training a model on a grid of possible hyperparameter values and taking the one that performs best on a validation sample (grid search).
Hadi S. Jomaa +2 more
openaire +2 more sources
Reinforcement Learning in RDPs by Combining Deep RL with Automata Learning
, 2023 Regular Decision Processes (RDPs) are a recently introduced model for decision-making in non-Markovian domains in which states are not postulated a-priori, and the next observation depends in a regular manner on past history. As such, they provide a more succinct and understandable model of the dynamics and reward function.
Tal Shahar, Ronen I. Brafman
openaire +1 more source
A neural model of hierarchical reinforcement learning. [PDF]
PLoS ONE, 2017 We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences ...
Daniel Rasmussen +2 more
doaj +1 more source