Results 41 to 50 of about 6,522,305 (296)
Time representation in reinforcement learning models of the basal ganglia [PDF]
, 2013 Reinforcement learning (RL) models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection.
Sebastian eDupraz +29 more
core +1 more source
Reinforcement-Learning-Based IDS for 6LoWPAN
, 2021 The Routing Protocol for low power Lossy networks (RPL) is a critical operational component of low power wireless personal area networks using IPv6 (6LoWPANs).
Aryan Mohammadi Pasikhani (11456185) +2 more
core +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
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
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
Journal of Information Systems Engineering and Business Intelligence, 2021 Background: Inventory policy highly influences Supply Chain Management (SCM) process. Evidence suggests that almost half of SCM costs are set off by stock-related expenses. Objective: This paper aims to minimise total inventory cost in SCM by applying a
Ika Nurkasanah
doaj +1 more source
Assured RL: Reinforcement Learning with Almost Sure Constraints
CoRR, 2020 We consider the problem of finding optimal policies for a Markov Decision Process with almost sure constraints on state transitions and action triplets. We define value and action-value functions that satisfy a barrier-based decomposition which allows for the identification of feasible policies independently of the reward process.
Agustin Castellano +2 more
openaire +2 more sources
Geometric Reinforcement Learning for Robotic Manipulation
IEEE Access, 2023 Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment.
Naseem Alhousani +5 more
doaj +1 more source
IEEE Access, 2020 Reinforcement learning (RL) is a learning technique that enables state-dependent learning through feedback from an environment and makes an action decision for maximizing a reward without prior knowledge of the environment.
Jaehyoung Park, Dong Seong Kim, Hyuk Lim
doaj +1 more source
Disentangled representations for improved generalisation in deep reinforcement learning [PDF]
Real-world environments are diverse and unpredictable, so Reinforcement Learning (RL) agents need to be robust to environment changes and adapt quickly.
Dunion, Mhairi
core +1 more source