The hierarchical interaction between the actor and critic in actor-critic based reinforcement
learning algorithms naturally lends itself to a game-theoretic interpretation. We adopt this …

In multi-agent coverage control problems, agents navigate their environment to reach
locations that maximize the coverage of some density. In practice, the density is rarely …

In reinforcement learning (RL), rewards of states are typically considered additive, and
following the Markov assumption, they are $\textit {independent} $ of states visited …

The lack of effective defense resource allocation strategies and reliable multi-agent
collaboration mechanisms lead to the low stability of Deep Reinforcement Learning (DRL) …

Currently, important privacy data of the Internet of Things (IoT) face extremely high risks of
leakage. Attackers persistently engage in continuous attacks on terminal devices to obtain …

Deep reinforcement learning (RL) methods provide state-of-art performance in complex
control tasks. However, it has been widely recognized that RL methods often fail to …

This paper addresses a continuous-time risk-minimizing two-player zero-sum stochastic
differential game (SDG), in which each player aims to minimize its probability of failure …

We present a self-improving, Neural Tree Expansion (NTE) method for multi-robot online
planning in non-cooperative environments, where each robot attempts to maximize its …

Autocurricular training is an important sub-area of multi-agent reinforcement learning
(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving …

Green Security Games (GSGs) have been successfully used in the protection of valuable
resources such as fisheries, forests and wildlife. While real-world deployment involves both …