Scalable Planning and Learning for Multiagent POMDPs: Extended Version

04/04/2014
by   Chris Amato, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/19/2020

Dream and Search to Control: Latent Space Planning for Continuous Control

Learning and planning with latent space dynamics has been shown to be us...
research
02/08/2023

Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning

A practical challenge in reinforcement learning are combinatorial action...
research
06/05/2018

The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces

Dyna is an architecture for reinforcement learning agents that interleav...
research
10/27/2021

Reinforcement Learning in Factored Action Spaces using Tensor Decompositions

We present an extended abstract for the previously published work TESSER...
research
04/02/2019

Planning with Expectation Models

Distribution and sample models are two popular model choices in model-ba...
research
02/24/2017

Scalable Multiagent Coordination with Distributed Online Open Loop Planning

We propose distributed online open loop planning (DOOLP), a general fram...
research
05/09/2012

Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal Decision Making

We introduce a challenging real-world planning problem where actions mus...

Please sign up or login with your details

Forgot password? Click here to reset