# An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types

## TL;DR

This paper empirically investigates how prior beliefs over policies influence the performance of multiagent learning algorithms in repeated interactions, highlighting their significance and potential for automatic computation.

## Contribution

It provides the first comprehensive empirical analysis of the practical impact of prior beliefs over policies and demonstrates methods for automatic prior belief computation.

## Key findings

- Prior beliefs significantly affect long-term performance.
- The impact varies with the depth of the planning horizon.
- Automatic methods can reliably compute prior beliefs.

## Abstract

Many multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algorithms which compute posterior beliefs over a hypothesised set of policies, based on the observed actions of the other agents. The posterior belief is complemented by the prior belief, which specifies the subjective likelihood of policies before any actions are observed. In this paper, we present the first comprehensive empirical study on the practical impact of prior beliefs over policies in repeated interactions. We show that prior beliefs can have a significant impact on the long-term performance of such methods, and that the magnitude of the impact depends on the depth of the planning horizon. Moreover, our results demonstrate that automatic methods can be used to compute prior beliefs with consistent performance effects. This indicates that prior beliefs could be eliminated as a manual parameter and instead be computed automatically.