Abstract—We introduce and establish the convergence of a distributed actor-critic method that orchestrates the coordination of multiple agents solving a general class of a Markov decision problem. The method leverages the centralized single-agent actor-critic algorithm of [1] and uses a consensus-like algorithm for updating agents ’ policy parameters. As an application and to validate our approach we consider a reward collection problem as an instance of a multi-agent coordination problem in a partially known environment and subject to dynamical changes and communication constraints. Index Terms—Markov decision processes, actor-critic methods, consensus, sensor networks, multi-agent coordination. I
Applications of deep reinforcement learning in multi-agent systems are a rapidly developing scientif...
We investigate three intertwined problems concerned with distributed cooperative control of groups o...
Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforc...
This dissertation is concerned with distributed decision making in networked multi-agent systems; th...
Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized traini...
In the past few year, the research community has paid much attention to consensus problems in multi-...
a large number of heterogeneous nodes called sensors and actors. The collaborative operation of sens...
Many cooperative behaviors of multi-agent teams emerge from local interactions among the agents, whe...
Abstract—In this paper, coordination and communication problems in Wireless Sensor and Actor Network...
This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent net...
During the last decade, the problem of consensus in Multi-Agent Systems (MASs) has been studied with...
The field of convention emergence studies how agents involved in repeated coordination games can rea...
This paper proposes a novel distributed algorithm for a multi-agent assignment problem, in which a g...
International audienceRecent works on multi-agent sequential decision mak- ing using decentralized p...
In this thesis several topics on consensus and gossip algorithms for multi-agent systems are address...
Applications of deep reinforcement learning in multi-agent systems are a rapidly developing scientif...
We investigate three intertwined problems concerned with distributed cooperative control of groups o...
Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforc...
This dissertation is concerned with distributed decision making in networked multi-agent systems; th...
Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized traini...
In the past few year, the research community has paid much attention to consensus problems in multi-...
a large number of heterogeneous nodes called sensors and actors. The collaborative operation of sens...
Many cooperative behaviors of multi-agent teams emerge from local interactions among the agents, whe...
Abstract—In this paper, coordination and communication problems in Wireless Sensor and Actor Network...
This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent net...
During the last decade, the problem of consensus in Multi-Agent Systems (MASs) has been studied with...
The field of convention emergence studies how agents involved in repeated coordination games can rea...
This paper proposes a novel distributed algorithm for a multi-agent assignment problem, in which a g...
International audienceRecent works on multi-agent sequential decision mak- ing using decentralized p...
In this thesis several topics on consensus and gossip algorithms for multi-agent systems are address...
Applications of deep reinforcement learning in multi-agent systems are a rapidly developing scientif...
We investigate three intertwined problems concerned with distributed cooperative control of groups o...
Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforc...