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 multi-agent decision making: a utility-based analysis and survey
The majority of multi-agent system implementations aim to optimise agents' policies with
respect to a single objective, despite the fact that many real-world problem domains are …
respect to a single objective, despite the fact that many real-world problem domains are …
Reinforcement learning approaches for specifying ordering policies of perishable inventory systems
A Kara, I Dogan - Expert Systems with Applications, 2018 - Elsevier
In this study, we deal with the inventory management system of perishable products under
the random demand and deterministic lead time in order to minimize the total cost of a …
the random demand and deterministic lead time in order to minimize the total cost of a …
MO-MIX: Multi-objective multi-agent cooperative decision-making with deep reinforcement learning
Deep reinforcement learning (RL) has been applied extensively to solve complex decision-
making problems. In many real-world scenarios, tasks often have several conflicting …
making problems. In many real-world scenarios, tasks often have several conflicting …
Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning
Multi-objective optimization, such as quality, productivity, and cost, of the textile
manufacturing process is increasingly challenging because of the growing complexity …
manufacturing process is increasingly challenging because of the growing complexity …
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 …
[HTML][HTML] Solving an energy resource management problem with a novel multi-objective evolutionary reinforcement learning method
GMC Leite, S Jiménez-Fernández… - Knowledge-Based …, 2023 - Elsevier
Microgrids have become popular candidates for integrating diverse energy sources into the
power grid as means of reducing fossil fuel usage. Energy Resource Management (ERM) is …
power grid as means of reducing fossil fuel usage. Energy Resource Management (ERM) is …
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 …