Learning Embeddings for Sequential Tasks Using Population of Agents
Mridul Mahajan Georgios Tzannetos Goran Radanovic Adish Singla
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 to each other 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 population
of agents to measure similarity between tasks in sequential decision-making
settings. In addition to qualitative assessment, we empirically demonstrate the
effectiveness of our techniques based on task embeddings by quantitative
comparisons against strong baselines on two application scenarios: predicting
an agent's performance on a test task by observing its performance on a small
quiz of tasks, and selecting tasks with desired characteristics from a given
set of options.