Results 51 to 60 of about 6,944,236 (307)
Maximum Power Point Tracking of Photovoltaic System Based on Reinforcement Learning
Sensors, 2019 The maximum power point tracking (MPPT) technique is often used in photovoltaic (PV) systems to extract the maximum power in various environmental conditions.
Kuan-Yu Chou +2 more
doaj +1 more source
Flatland-RL : Multi-Agent Reinforcement Learning on Trains
CoRR, 2020 Efficient automated scheduling of trains remains a major challenge for modern railway systems. The underlying vehicle rescheduling problem (VRSP) has been a major focus of Operations Research (OR) since decades. Traditional approaches use complex simulators to study VRSP, where experimenting with a broad range of novel ideas is time consuming and has a
Sharada Prasanna Mohanty +13 more
openaire +2 more sources
A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
Simplified Temporal Consistency Reinforcement Learning
, 2023 Reinforcement learning (RL) is able to solve complex sequential decision-making tasks but is currently limited by sample efficiency and required computation.
Pajarinen, Joni +4 more
core +1 more source
A Comprehensive Study on Reinforcement Learning and Deep Reinforcement Learning Schemes
Sir Syed University Research Journal of Engineering and TechnologyReinforcement learning (RL) has emerged as a powerful tool for creating artificial intelligence systems (AIS) and solving problems which require sequential decision-making. Reinforcement learning has achieved some impressive achievements in recent years,
Muhammad Azhar +4 more
doaj +1 more source
CARL: A Synergistic Framework for Causal Reinforcement Learning
IEEE Access, 2023 Causal Reinforcement Learning (CRL) is an emerging field where two essential areas for the development of artificial intelligence are integrated. Existing works in the area have shown how causality can contribute to mitigate some of the limitations of ...
Arquimides Mendez-Molina +2 more
doaj +1 more source
Advanced Functional Materials, EarlyView.Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner +14 more
wiley +1 more source
Future InternetFog computing extends cloud services to the network edge, enabling low-latency processing for Internet of Things (IoT) applications. However, this distributed approach is vulnerable to a wide range of attacks, necessitating advanced intrusion detection ...
Nyashadzashe Tamuka +5 more
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
Sample Efficient Reinforcement Learning with Active Learning for Molecular Design [PDF]
, 2023 Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific ...
Jon Paul, Janet +5 more
core +1 more source