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A survey of progress on cooperative multi-agent reinforcement learning in open environment
Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
Decision making in open agent systems
In many real‐world applications of AI, the set of actors and tasks are not constant, but
instead change over time. Robots tasked with suppressing wildfires eventually run out of …
instead change over time. Robots tasked with suppressing wildfires eventually run out of …
Fast teammate adaptation in the presence of sudden policy change
Cooperative multi-agent reinforcement learning (MARL), where agents coordinates with
teammate (s) for a shared goal, may sustain non-stationary caused by the policy change of …
teammate (s) for a shared goal, may sustain non-stationary caused by the policy change of …
Open human-robot collaboration using decentralized inverse reinforcement learning
The growing interest in human-robot collaboration (HRC), where humans and robots
cooperate towards shared goals, has seen significant advancements over the past decade …
cooperate towards shared goals, has seen significant advancements over the past decade …
[PDF][PDF] Observer-aware planning with implicit and explicit communication
Communication of intentions, goals, and desires is integral to our daily interactions, making
them essential for autonomous agents. Communication can manifest itself in both implicit …
them essential for autonomous agents. Communication can manifest itself in both implicit …
Open Human-Robot Collaboration Systems (OHRCS): A Research Perspective
Human-robot collaboration (HRC) is the paradigm of humans and robots working
synergistically in a shared workspace toward common goals. Prior research models such …
synergistically in a shared workspace toward common goals. Prior research models such …
Combining a Meta-Policy and Monte-Carlo Planning for Scalable Type-Based Reasoning in Partially Observable Environments
The design of autonomous agents that can interact effectively with other agents without prior
coordination is a core problem in multi-agent systems. Type-based reasoning methods …
coordination is a core problem in multi-agent systems. Type-based reasoning methods …
[PDF][PDF] Bayes-Adaptive Monte-Carlo Planning for Type-Based Reasoning in Large Partially Observable, Multi-Agent Environments
Designing autonomous agents that can interact effectively with other agents without prior
coordination is an important problem in multi-agent systems. Type-based reasoning …
coordination is an important problem in multi-agent systems. Type-based reasoning …
United We Stand: Decentralized Multi-Agent Planning With Attrition
Decentralized planning is a key element of cooperative multi-agent systems for information
gathering tasks. However, despite the high frequency of agent failures in realistic large …
gathering tasks. However, despite the high frequency of agent failures in realistic large …
[PDF][PDF] Modeling Cognitive Biases in Decision-Theoretic Planning for Active Cyber Deception
This paper presents an approach to modeling and exploiting cognitive biases of cyber
attackers in planning for active deception. Sophisticated cyber attacks are primarily …
attackers in planning for active deception. Sophisticated cyber attacks are primarily …