[HTML][HTML] Precision irrigation management using machine learning and digital farming solutions
Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to
compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the …
compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the …
A review of reinforcement learning for autonomous building energy management
K Mason, S Grijalva - Computers & Electrical Engineering, 2019 - Elsevier
The area of building energy management has received a significant amount of interest in
recent years. This area is concerned with combining advancements in sensor technologies …
recent years. This area is concerned with combining advancements in sensor technologies …
Deep reinforcement learning for autonomous driving: A survey
With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …
(RL) has become a powerful learning framework now capable of learning complex policies …
Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants
Particle swarm optimisation (PSO) is a bio-inspired swarm based approach to solving
optimisation problems. The algorithm functions as a result of particles traversing and …
optimisation problems. The algorithm functions as a result of particles traversing and …
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years,
with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed …
with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed …
Reward shaping for knowledge-based multi-objective multi-agent reinforcement learning
The majority of multi-agent reinforcement learning (MARL) implementations aim to optimize
systems with respect to a single objective, despite the fact that many real-world problems are …
systems with respect to a single objective, despite the fact that many real-world problems are …
Policy invariance under reward transformations for multi-objective reinforcement learning
Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm,
where an agent learns to improve its performance in an environment by maximising a …
where an agent learns to improve its performance in an environment by maximising a …
A network aware approach for the scheduling of virtual machine migration during peak loads
Live virtual machine migration can have a major impact on how a cloud system performs, as
it consumes significant amounts of network resources such as bandwidth. Migration …
it consumes significant amounts of network resources such as bandwidth. Migration …
A meta optimisation analysis of particle swarm optimisation velocity update equations for watershed management learning
Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to
address hard optimisation problems. The algorithm operates as a result of a number of …
address hard optimisation problems. The algorithm operates as a result of a number of …
Data driven hybrid edge computing-based hierarchical task guidance for efficient maritime escorting with multiple unmanned surface vehicles
The advancement of hardware and software technology makes multiple cooperative
unmanned surface vehicles (USVs) utilized in maritime escorting with low cost and high …
unmanned surface vehicles (USVs) utilized in maritime escorting with low cost and high …