Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …
solve various sequential decision-making problems. These algorithms, however, have faced …
Multi-agent deep reinforcement learning: a survey
The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Although the multi-agent domain has been overshadowed by its single-agent counterpart …
A survey on transfer learning for multiagent reinforcement learning systems
Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …
other agents through autonomous exploration of the environment. However, learning a …
More than privacy: Applying differential privacy in key areas of artificial intelligence
Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However,
alongside all its advancements, problems have also emerged, such as privacy violations …
alongside all its advancements, problems have also emerged, such as privacy violations …
Shared experience actor-critic for multi-agent reinforcement learning
Exploration in multi-agent reinforcement learning is a challenging problem, especially in
environments with sparse rewards. We propose a general method for efficient exploration by …
environments with sparse rewards. We propose a general method for efficient exploration by …
Agents teaching agents: a survey on inter-agent transfer learning
While recent work in reinforcement learning (RL) has led to agents capable of solving
increasingly complex tasks, the issue of high sample complexity is still a major concern. This …
increasingly complex tasks, the issue of high sample complexity is still a major concern. This …
[HTML][HTML] Deep reinforcement Learning Challenges and Opportunities for Urban Water Systems.
The efficient and sustainable supply and transport of water is a key component to any
functioning civilisation making the role of urban water systems (UWS) inherently crucial to …
functioning civilisation making the role of urban water systems (UWS) inherently crucial to …
Applying differential privacy mechanism in artificial intelligence
Artificial Intelligence (AI) has attracted a large amount of attention in recent years. However,
several new problems, such as privacy violations, security issues, or effectiveness, have …
several new problems, such as privacy violations, security issues, or effectiveness, have …
Differential advising in multiagent reinforcement learning
Agent advising is one of the main approaches to improve agent learning performance by
enabling agents to share advice. Existing advising methods have a common limitation that …
enabling agents to share advice. Existing advising methods have a common limitation that …
Differentially private malicious agent avoidance in multiagent advising learning
Agent advising is one of the key approaches to improve agent learning performance by
enabling agents to ask for advice between each other. Existing agent advising approaches …
enabling agents to ask for advice between each other. Existing agent advising approaches …