# Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment

arXiv: https://arxiv.org/abs/2007.03363 · HTML: https://mlsanity.com/paper/2007.03363/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications](https://mlsanity.com/paper/2408.10215/) (2024)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Persistent Rule-based Interactive Reinforcement Learning](https://mlsanity.com/paper/2102.02441/) (2021)
- [Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios](https://mlsanity.com/paper/2207.03214/) (2022)
- [Guiding Reinforcement Learning Using Uncertainty-Aware Large Language Models](https://mlsanity.com/paper/2411.14457/) (2024)
- [Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review](https://mlsanity.com/paper/2505.20503/) (2025)
- [Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control](https://mlsanity.com/paper/2505.23355/) (2025)
- [Modelling resource allocation in uncertain system environment through deep reinforcement learning](https://mlsanity.com/paper/2106.09461/) (2021)
- [NNPP: A Learning-Based Heuristic Model for Accelerating Optimal Path Planning on Uneven Terrain](https://mlsanity.com/paper/2308.04792/) (2023)
- [Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs](https://mlsanity.com/paper/2606.18747/) (2026)

## Abstract (arXiv)

Robots are extending their presence in domestic environments every day, being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to increasingly perform more complex tasks and, therefore, be able to acquire experience from different sources as quickly as possible. A plausible approach to address this issue is interactive feedback, where a trainer advises a learner on which actions should be taken from specific states to speed up the learning process. Moreover, deep reinforcement learning has been recently widely utilized in robotics to learn the environment and acquire new skills autonomously. However, an open issue when using deep reinforcement learning is the excessive time needed to learn a task from raw input images. In this work, we propose a deep reinforcement learning approach with interactive feedback to learn a domestic task in a human-robot scenario. We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL). We demonstrate that interactive approaches provide advantages for the learning process. The obtained results show that a learner agent, using either agent-IDeepRL or human-IDeepRL, completes the given task earlier and has fewer mistakes compared to the autonomous DeepRL approach.