Curiosity-driven and victim-aware adversarial policies
Recent years have witnessed great potential in applying Deep Reinforcement Learning
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …
Inducing stackelberg equilibrium through spatio-temporal sequential decision-making in multi-agent reinforcement learning
In multi-agent reinforcement learning (MARL), self-interested agents attempt to establish
equilibrium and achieve coordination depending on game structure. However, existing …
equilibrium and achieve coordination depending on game structure. However, existing …
Stochastic graph neural network-based value decomposition for marl in internet of vehicles
Autonomous driving has witnessed incredible advances in the past several decades, while
Multi-Agent Reinforcement Learning (MARL) promises to satisfy the essential need of …
Multi-Agent Reinforcement Learning (MARL) promises to satisfy the essential need of …
Stochastic graph neural network-based value decomposition for multi-agent reinforcement learning in urban traffic control
Multi-Agent Reinforcement Learning (MARL) has reached astonishing achievements in
various fields such as the traffic control of vehicles in a wireless connected environment. In …
various fields such as the traffic control of vehicles in a wireless connected environment. In …
Efficient policy generation in multi-agent systems via hypergraph neural network
The application of deep reinforcement learning in multi-agent systems introduces extra
challenges. In a scenario with numerous agents, one of the most important concerns …
challenges. In a scenario with numerous agents, one of the most important concerns …
Learning multi-agent coordination through connectivity-driven communication
In artificial multi-agent systems, the ability to learn collaborative policies is predicated upon
the agents' communication skills: they must be able to encode the information received from …
the agents' communication skills: they must be able to encode the information received from …
Mastering Complex Coordination Through Attention-Based Dynamic Graph
The coordination between agents in multi-agent systems has become a popular topic in
many fields. To catch the inner relationship between agents, the graph structure is combined …
many fields. To catch the inner relationship between agents, the graph structure is combined …
Learning to communicate in cooperative multi-agent reinforcement learning
E Pesce - 2023 - wrap.warwick.ac.uk
Recent advances in deep reinforcement learning have produced unprecedented results.
The success obtained on single-agent applications led to exploring these techniques in the …
The success obtained on single-agent applications led to exploring these techniques in the …
[PDF][PDF] Curiosity-driven and victim-aware adversarial policies.(2022)
Recent years have witnessed great potential in applying Deep Reinforcement Learning
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …