Curriculum goal masking for continuous deep reinforcement learning

TL;DR

This paper introduces a goal masking method for deep reinforcement learning that estimates goal difficulty and enables curriculum learning, improving performance in robotic manipulation tasks.

Contribution

It presents a novel goal masking technique that optimizes goal sampling based on difficulty, enhancing deep RL training efficiency and effectiveness.

Findings

Focusing on medium difficulty goals benefits DDPG performance.

Sampling hard goals more frequently improves DDPG with HER.

The method outperforms standard goal sampling in robotic manipulation tasks.

Abstract

Deep reinforcement learning has recently gained a focus on problems where policy or value functions are independent of goals. Evidence exists that the sampling of goals has a strong effect on the learning performance, but there is a lack of general mechanisms that focus on optimizing the goal sampling process. In this work, we present a simple and general goal masking method that also allows us to estimate a goal's difficulty level and thus realize a curriculum learning approach for deep RL. Our results indicate that focusing on goals with a medium difficulty level is appropriate for deep deterministic policy gradient (DDPG) methods, while an "aim for the stars and reach the moon-strategy", where hard goals are sampled much more often than simple goals, leads to the best learning performance in cases where DDPG is combined with for hindsight experience replay (HER). We demonstrate that the approach significantly outperforms standard goal sampling for different robotic object manipulation problems.