Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency

TL;DR

This paper introduces a method that leverages learned primitive behaviors to guide exploration in reinforcement learning, significantly improving sample efficiency and training speed in goal-based robotic tasks compared to standard HER.

Contribution

The paper proposes a novel approach that uses a critic network to selectively incorporate primitive behaviors, enhancing exploration and learning efficiency in HER-based reinforcement learning.

Findings

Faster learning of successful policies with primitive behavior guidance

Improved sample efficiency over standard HER

Reduced training time in block manipulation tasks

Abstract

Hindsight Experience Replay (HER) is a technique used in reinforcement learning (RL) that has proven to be very efficient for training off-policy RL-based agents to solve goal-based robotic manipulation tasks using sparse rewards. Even though HER improves the sample efficiency of RL-based agents by learning from mistakes made in past experiences, it does not provide any guidance while exploring the environment. This leads to very large training times due to the volume of experience required to train an agent using this replay strategy. In this paper, we propose a method that uses primitive behaviours that have been previously learned to solve simple tasks in order to guide the agent toward more rewarding actions during exploration while learning other more complex tasks. This guidance, however, is not executed by a manually designed curriculum, but rather using a critic network to decide at each timestep whether or not to use the actions proposed by the previously-learned primitive policies. We evaluate our method by comparing its performance against HER and other more efficient variations of this algorithm in several block manipulation tasks. We demonstrate the agents can learn a successful policy faster when using our proposed method, both in terms of sample efficiency and computation time. Code is available at https://github.com/franroldans/qmp-her.