  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.
