Learning Embeddings for Sequential Tasks Using Population of Agents

arXiv:2306.03311v2 Announce Type: replace
Abstract: We present an information-theoretic framework to learn fixed-dimensional embeddings for tasks in reinforcement learning. We leverage the idea that two tasks are similar if observing an agent's performance on one task reduces our uncertainty about its performance on the other. This intuition is captured by our information-theoretic criterion which uses a diverse agent population as an approximation for the space of agents to measure similarity between tasks in sequential decision-making settings. In addition to qualitative …

abstract agent agents arxiv cs.ai cs.lg embeddings framework information intuition learn performance population reinforcement reinforcement learning s performance tasks type uncertainty