Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators

TL;DR

This paper introduces a method that integrates semantic knowledge via Knowledge Graph Embeddings into Deep Reinforcement Learning to significantly reduce training time and improve accuracy in robotic manipulation tasks.

Contribution

It presents a novel approach combining semantic knowledge with DRL, enhancing learning efficiency without additional computational costs.

Findings

Achieves up to 60% reduction in learning time

Improves task accuracy by approximately 15 percentage points

Demonstrates effectiveness in environments with fixed and randomized targets

Abstract

Deep Reinforcement Learning (DRL) is a powerful framework for solving complex sequential decision-making problems, particularly in robotic control. However, its practical deployment is often hindered by the substantial amount of experience required for learning, which results in high computational and time costs. In this work, we propose a novel integration of DRL with semantic knowledge in the form of Knowledge Graph Embeddings (KGEs), aiming to enhance learning efficiency by providing contextual information to the agent. Our architecture combines KGEs with visual observations, enabling the agent to exploit environmental knowledge during training. Experimental validation with robotic manipulators in environments featuring both fixed and randomized target attributes demonstrates that our method achieves up to {60}{\%} reduction in learning time and improves task accuracy by approximately 15 percentage points, without increasing training time or computational complexity. These results highlight the potential of semantic knowledge to reduce sample complexity and improve the effectiveness of DRL in robotic applications.