Results 21 to 30 of about 6,522,305 (296)

Dueling RL: Reinforcement Learning with Trajectory Preferences

CoRR, 2021
Aadirupa Saha and Aldo Pacchiano contributed ...
Aadirupa Saha, Aldo Pacchiano, Jonathan Lee 0002   +2 more
openaire   +3 more sources

Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle

IEEE Access, 2020
Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning or pre-programming tasks.
Qilei Zhang, Jinying Lin, Qixin Sha, Bo He, Guangliang Li   +4 more
doaj   +1 more source

CST-RL: Contrastive Spatio-Temporal Representations for Reinforcement Learning

IEEE Access, 2023
Learning representations from high-dimensional observations is critical for training of pixel-based continuous control tasks with reinforcement learning (RL).
Chi-Kai Ho, Chung-Ta King
doaj   +1 more source

Learning the Quadruped Robot by Reinforcement Learning (RL)

Iraqi Journal for Electrical and Electronic Engineering, 2022
In this paper, a simulation was utilized to create and test the suggested controller and to investigate the ability of a quadruped robot based on the SimScape-Multibody toolbox, with PID controllers and deep deterministic policy gradient DDPG Reinforcement learning (RL) techniques.
A. Issa, A. Aldair
openaire   +1 more source

Safe and Efficient Operation with Constrained Hierarchical Reinforcement Learning

, 2023
Hierarchical Reinforcement Learning (HRL) holds the promise of enhancing sample efficiency and generalization capabilities of Reinforcement Learning (RL) agents by leveraging task decomposition and temporal abstraction, which aligns with human reasoning.
Günnemann, Stephan, Roscher, Karsten, Schmoeller da Roza, Felippe   +2 more
core   +1 more source

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

A review of reinforcement learning based hyper-heuristics [PDF]

PeerJ Computer Science
The reinforcement learning based hyper-heuristics (RL-HH) is a popular trend in the field of optimization. RL-HH combines the global search ability of hyper-heuristics (HH) with the learning ability of reinforcement learning (RL). This synergy allows the
Cuixia Li, Xiang Wei, Jing Wang, Shuozhe Wang, Shuyan Zhang   +4 more
doaj   +2 more sources

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, Walter Senn, Robert Urbanczik, Senn, W   +3 more
core   +1 more source

RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$

CoRR, 2023
Meta reinforcement learning (Meta-RL) methods such as RL$^2$ have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution. However, they show poor asymptotic performance and struggle with out-of-distribution tasks because they rely on sequence models, such as recurrent neural networks or ...
Abhinav Bhatia, Samer B. Nashed, Shlomo Zilberstein   +2 more
openaire   +2 more sources

A Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes

IEEE Access, 2018
In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power.
Tuyen P. Le, Ngo Anh Vien, TaeChoong Chung   +2 more
doaj   +1 more source