Reinforcement Learning

Oct 19, 2024

The project "MRTAgent" is a self-play-driven bi-level reinforcement learning framework, designed to address the growing complexity of task allocation and robot selection in dynamic warehouse environments. As warehouse automation becomes increasingly prevalent, optimizing the assignment of tasks to robots while accounting for practical constraints such as energy consumption, battery life, and avoiding collisions becomes crucial. MRTAgent was developed to minimize inefficiencies such as unnecessary travel distances and delays in task completion. The framework utilizes a dual-agent approach, where one agent focuses on task selection and the other on robot selection. By separating these tasks and having one agent in evaluation mode while the other trains, MRTAgent ensures that real-time decisions can be made efficiently. This structure allows for continuous learning and adaptation in environments with constantly changing conditions. To support safe navigation, the system integrates a modified Linear Quadratic Regulator (LQR) for collision-free robot movement, accommodating physical constraints present in real-world scenarios. MRTAgent was rigorously tested across multiple datasets with varying environmental conditions, demonstrating substantial improvements in reducing operational costs and delays compared to traditional methods. This innovative framework enables more intelligent task management, helping warehouses run more smoothly while ensuring safety and efficiency.

Oct 18, 2024

Happy to share that I will present our AAAI work at the LCC organised by CSE and Systems & Control department at IIT Bombay. The logistics of the presentation will be shared soon

Aug 1, 2024

Happy to share that we have been awarded the NASSCOM AI Gamechangers Award 2024 in the AI Research category for our work on reinforcement learning-based control of power networks. It’s a great honor to be among the top ten research projects.!

Jul 24, 2024

Happy to share that our work data-driven control and management of power networks has been awarded with the certificate of merit by the India Smart Grid Forum. This work was published as main track in AAAI 2023.

Feb 25, 2024

Jan 4, 2024

Jul 7, 2023

Jun 26, 2023

Power grids transport electricity across states, countries, and even continents. They are the backbone of global societies and economies, playing a pivotal economic and societal role by supplying reliable power to industry, business, and domestic consumers. Their importance is even more critical today as we transition toward a sustainable world within a carbon-free economy. Issues within the power grid can range from transient stability problems and localized blackouts to complete system or country-wide blackouts, which can cause significant economic and social disruptions. Grid operators are responsible for ensuring a secure supply of electricity at all times and designing systems to be both reliable and resilient. With the advent of renewable energy, electric mobility, and limitations on new grid infrastructure projects, the task of controlling existing grids is becoming increasingly difficult, forcing operators to do ‘more with less.’ We have developed RL-based solutions to address this important real-world problem by alleviating congestion in power networks through topological reconfigurations.

Feb 7, 2023

Dec 2, 2022