Results 31 to 40 of about 6,522,305 (296)
Co-Evolution of Predator-Prey Ecosystems by Reinforcement Learning Agents
Entropy, 2021 The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. Since analyzing and accurately predicting the intelligent adaptation of multiple species is difficult due to their ...
Jeongho Park +4 more
doaj +1 more source
A new model of decision processing in instrumental learning tasks
eLife, 2021 Learning and decision-making are interactive processes, yet cognitive modeling of error-driven learning and decision-making have largely evolved separately.
Steven Miletić +5 more
doaj +1 more source
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-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
Advances in Neural Information Processing Systems 36, 2023 Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization. Despite the focus on algorithms aimed at resolving visual generalization problems, we argue that the devil is in the existing benchmarks as they are restricted to isolated ...
Zhecheng Yuan +6 more
openaire +3 more sources
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
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
Inductive biases and generalisation for deep reinforcement learning [PDF]
, 2021 In this thesis we aim to improve generalisation in deep reinforcement learning. Generalisation is a fundamental challenge for any type of learning, determining how acquired knowledge can be transferred to new, previously unseen situations.
Igl, Maximilian
core +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
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