1 code implementation • 10 May 2023 • Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li
We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability.
1 code implementation • NeurIPS 2023 • Yulun Zhang, Matthew C. Fontaine, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li
We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2, 350 robots.
1 code implementation • 3 Mar 2021 • Varun Bhatt, Michael Buro
In this paper, we first study learning in matrix-based signaling games to empirically show that decentralized methods can converge to a suboptimal policy.
1 code implementation • 26 Apr 2023 • Varun Bhatt, Heramb Nemlekar, Matthew C. Fontaine, Bryon Tjanaka, Hejia Zhang, Ya-Chuan Hsu, Stefanos Nikolaidis
In the shared control teleoperation domain and a more complex shared workspace collaboration task, we show that surrogate assisted scenario generation efficiently synthesizes diverse datasets of challenging scenarios.
no code implementations • 9 Mar 2020 • Varun Bhatt, Shalini Shrivastava, Tanmay Chavan, Udayan Ganguly
The in-memory computing paradigm with emerging memory devices has been recently shown to be a promising way to accelerate deep learning.
no code implementations • 7 Feb 2021 • Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat, Varun Bhatt, Harshith Goka, Archit Gupta, Kalpesh Krishna, Vihari Piratla
In this paper, we investigate the role of the parameter $d$ in RL; $d$ is called the "frame-skip" parameter, since states in the Atari domain are images.
no code implementations • 9 Jun 2022 • Varun Bhatt, Bryon Tjanaka, Matthew C. Fontaine, Stefanos Nikolaidis
Results in two benchmark domains show that DSAGE significantly outperforms existing QD environment generation algorithms in discovering collections of environments that elicit diverse behaviors of a state-of-the-art RL agent and a planning agent.
no code implementations • 2 Feb 2024 • Yulun Zhang, He Jiang, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li
Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in four benchmark maps, and (2) our update model can generate guidance graphs for as large as $93 \times 91$ maps and as many as 3000 agents.