An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types
This addresses the challenge of quick learning with unknown agents in multiagent systems, though it is incremental as it builds on existing belief-based methods.
The paper tackles the problem of how prior beliefs over policies affect learning in multiagent interactions, showing that these priors significantly impact long-term performance depending on planning horizon depth, and that automatic computation can replace manual parameter tuning.
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.