Shared Learning : Enhancing Reinforcement in $Q$-Ensembles

TL;DR

This paper introduces Shared Learning, a framework that enhances data efficiency in Q-ensemble reinforcement learning algorithms by facilitating transfer of value estimates, demonstrated to accelerate learning in Atari games.

Contribution

The paper proposes a novel Shared Learning framework that improves data efficiency in Q-ensemble algorithms by enabling transfer of value estimates across tasks.

Findings

Speeds up learning in Q-ensembles on Atari games

Minimal computational overhead introduced

Enhances transfer of information across tasks

Abstract

Deep Reinforcement Learning has been able to achieve amazing successes in a variety of domains from video games to continuous control by trying to maximize the cumulative reward. However, most of these successes rely on algorithms that require a large amount of data to train in order to obtain results on par with human-level performance. This is not feasible if we are to deploy these systems on real world tasks and hence there has been an increased thrust in exploring data efficient algorithms. To this end, we propose the Shared Learning framework aimed at making $Q$-ensemble algorithms data-efficient. For achieving this, we look into some principles of transfer learning which aim to study the benefits of information exchange across tasks in reinforcement learning and adapt transfer to learning our value function estimates in a novel manner. In this paper, we consider the special case of transfer between the value function estimates in the $Q$-ensemble architecture of BootstrappedDQN. We further empirically demonstrate how our proposed framework can help in speeding up the learning process in $Q$-ensembles with minimum computational overhead on a suite of Atari 2600 Games.