3,903 research outputs found
From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
A lot of efforts have been devoted to investigating how agents can learn
effectively and achieve coordination in multiagent systems. However, it is
still challenging in large-scale multiagent settings due to the complex
dynamics between the environment and agents and the explosion of state-action
space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning
(DyMA-CL) to solve large-scale problems by starting from learning on a
multiagent scenario with a small size and progressively increasing the number
of agents. We propose three transfer mechanisms across curricula to accelerate
the learning process. Moreover, due to the fact that the state dimension varies
across curricula,, and existing network structures cannot be applied in such a
transfer setting since their network input sizes are fixed. Therefore, we
design a novel network structure called Dynamic Agent-number Network (DyAN) to
handle the dynamic size of the network input. Experimental results show that
DyMA-CL using DyAN greatly improves the performance of large-scale multiagent
learning compared with state-of-the-art deep reinforcement learning approaches.
We also investigate the influence of three transfer mechanisms across curricula
through extensive simulations.Comment: Accepted by AAAI202
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems
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