# Influence-Augmented Online Planning for Complex Environments

arXiv: https://arxiv.org/abs/2010.11038 · HTML: https://mlsanity.com/paper/2010.11038/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems](https://mlsanity.com/paper/2202.01534/) (2022)
- [Online Planning in POMDPs with Self-Improving Simulators](https://mlsanity.com/paper/2201.11404/) (2022)
- [Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems](https://mlsanity.com/paper/2207.00288/) (2022)

## Abstract (arXiv)

How can we plan efficiently in real time to control an agent in a complex environment that may involve many other agents? While existing sample-based planners have enjoyed empirical success in large POMDPs, their performance heavily relies on a fast simulator. However, real-world scenarios are complex in nature and their simulators are often computationally demanding, which severely limits the performance of online planners. In this work, we propose influence-augmented online planning, a principled method to transform a factored simulator of the entire environment into a local simulator that samples only the state variables that are most relevant to the observation and reward of the planning agent and captures the incoming influence from the rest of the environment using machine learning methods. Our main experimental results show that planning on this less accurate but much faster local simulator with POMCP leads to higher real-time planning performance than planning on the simulator that models the entire environment.