A practical guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …
between multiple, often conflicting, objectives. Despite this, the majority of research in …
Liir: Learning individual intrinsic reward in multi-agent reinforcement learning
A great challenge in cooperative decentralized multi-agent reinforcement learning (MARL) is
generating diversified behaviors for each individual agent when receiving only a team …
generating diversified behaviors for each individual agent when receiving only a team …
Multi-agent reinforcement learning for online scheduling in smart factories
T Zhou, D Tang, H Zhu, Z Zhang - Robotics and computer-integrated …, 2021 - Elsevier
Rapid advances in sensing and communication technologies connect isolated
manufacturing units, which generates large amounts of data. The new trend of mass …
manufacturing units, which generates large amounts of data. The new trend of mass …
Human-guided reinforcement learning with sim-to-real transfer for autonomous navigation
Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …
applications, but limited computing resource makes it challenging to deploy a well-behaved …
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 …
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 …
Individual reward assisted multi-agent reinforcement learning
In many real-world multi-agent systems, the sparsity of team rewards often makes it difficult
for an algorithm to successfully learn a cooperative team policy. At present, the common way …
for an algorithm to successfully learn a cooperative team policy. At present, the common way …
Comprehensive overview of reward engineering and shaping in advancing reinforcement learning applications
Reinforcement Learning (RL) seeks to develop systems capable of autonomous decision-
making by learning through interaction with their environment. Central to this process are …
making by learning through interaction with their environment. Central to this process are …
A multi‐objective multi‐agent deep reinforcement learning approach to residential appliance scheduling
Residential buildings are large consumers of energy. They contribute significantly to the
demand placed on the grid, particularly during hours of peak demand. Demand‐side …
demand placed on the grid, particularly during hours of peak demand. Demand‐side …
Reward shaping in multiagent reinforcement learning for self-organizing systems in assembly tasks
Self-organizing systems feature flexibility and robustness for tasks that may endure changes
over time. Various methods, eg, applying task-field and social-field, have been proposed to …
over time. Various methods, eg, applying task-field and social-field, have been proposed to …