A study of multi-agent reinforcement learning with swarm intelligence
Cooperative multi-agent systems (MASs) are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility [25]. Cooperative multi-agent learning involves constructing the learning system so as to encourage cooperation among the agents in either conc...
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Format: | Theses and Dissertations |
Language: | English |
Published: |
2016
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Online Access: | http://hdl.handle.net/10356/69031 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Cooperative multi-agent systems (MASs) are ones in which several agents attempt, through
their interaction, to jointly solve tasks or to maximize utility [25]. Cooperative multi-agent
learning involves constructing the learning system so as to encourage cooperation among the
agents in either concurrent learning or teamwork [25]. In most of the MAS, cooperative learning
is usually realized by reinforcement learning (RL) due to the online and interactive behavior
of agents. On the other hand, while multi-agent learning research works have been reported
in a wide range of application domains, multi-agent reinforcement learning based on principles
from swarm intelligence has remained under-explored. To this end, the dissertation takes
an explorative attitude in the design of cooperative multi-agent reinforcement learning frameworks
by leveraging the emergent behaviors from swarm intelligence (SI). The current research
presented in this dissertation delves in the role of swarm intelligence algorithms in multi-agent
cooperative reinforcement learning.
The presented research works use SI-inspired approach on top of the RL agent learning
framework to provide both principles for construction of complex systems involving multiple
agents and mechanisms for coordination of independent agents’ behaviors. In this dissertation,
a self-organizing neural model called temporal difference-fusion architecture for learning and
cognition (TD-FALCON) [33, 34] is adopted as the RL agent. The synergy of TD-FALCON
and swarm intelligence was studied, by using multiple simultaneous learners, one to one or
more agents (concurrent learning), coordinated via rules derived from swarm intelligence. The
main contribution of this thesis includes a number of proposed learning approaches in which
agents learn by communicating via swarm intelligence to cooperatively solve tasks using different
distributed sensing and communication content as inspired by swarm behaviors such as
flocking formation and ant colony. Particularly, the current research proposed two SI-inspired
multi-agent reinforcement learning approaches, namely flocking-based cooperative learning MAS and pheromone-guided ant colony TD-FALCON network. The effectiveness of these
approaches are shown in the context of pursuit game and resource gathering problems. |
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