Learning to Explore with Meta-Policy Gradient

TL;DR

This paper introduces a meta-policy gradient method that learns adaptive exploration policies for off-policy reinforcement learning, enabling more effective global exploration and faster learning.

Contribution

It proposes a novel meta-policy gradient algorithm that learns exploration policies independent of the actor, improving sample efficiency in DDPG.

Findings

Significantly faster learning in various RL tasks.

Enhanced exploration capabilities beyond local regions.

Improved sample efficiency of DDPG.

Abstract

The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy. Existing exploration methods are mostly based on adding noise to the on-going actor policy and can only explore \emph{local} regions close to what the actor policy dictates. In this work, we develop a simple meta-policy gradient algorithm that allows us to adaptively learn the exploration policy in DDPG. Our algorithm allows us to train flexible exploration behaviors that are independent of the actor policy, yielding a \emph{global exploration} that significantly speeds up the learning process. With an extensive study, we show that our method significantly improves the sample-efficiency of DDPG on a variety of reinforcement learning tasks.