# A Comparative Analysis of Interactive Reinforcement Learning Algorithms in Warehouse Robot Grid Based Environment

arXiv: https://arxiv.org/abs/2407.11671 · HTML: https://mlsanity.com/paper/2407.11671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 1 of 1)

- [Interactive Learning from Policy-Dependent Human Feedback](https://mlsanity.com/paper/1701.06049/) (2017)

## Abstract (arXiv)

The field of warehouse robotics is currently in high demand, with major technology and logistics companies making significant investments in these advanced systems. Training robots to operate in such complex environments is challenging, often requiring human supervision for adaptation and learning. Interactive reinforcement learning (IRL) is a key training methodology in human-computer interaction. This paper presents a comparative study of two IRL algorithms: Q-learning and SARSA, both trained in a virtual grid-simulation-based warehouse environment. To maintain consistent feedback rewards and avoid bias, feedback was provided by the same individual throughout the study.