Search Results for author: Aakriti Agrawal

Found 7 papers, 3 papers with code

Benchmarking the Robustness of Image Watermarks

1 code implementation16 Jan 2024 Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, ChengHao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang

We present WAVES (Watermark Analysis Via Enhanced Stress-testing), a novel benchmark for assessing watermark robustness, overcoming the limitations of current evaluation methods. WAVES integrates detection and identification tasks, and establishes a standardized evaluation protocol comprised of a diverse range of stress tests.

Benchmarking

Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning

no code implementations12 Oct 2023 Aakriti Agrawal, Rohith Aralikatti, Yanchao Sun, Furong Huang

This work is the first to formulate the generalised problem of robustness to multi-modal environment uncertainty in MARL.

Multi-agent Reinforcement Learning

Learning When to Trust Which Teacher for Weakly Supervised ASR

no code implementations21 Jun 2023 Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke

We show the efficacy of our approach using LibriSpeech and LibriLight benchmarks and find an improvement of 4 to 25\% over baselines that uniformly weight all the experts, use a single expert model, or combine experts using ROVER.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments

no code implementations7 Sep 2022 Aakriti Agrawal, Senthil Hariharan, Amrit Singh Bedi, Dinesh Manocha

At the higher level, we solve the task allocation by formulating it in terms of Markov Decision Processes and choosing the appropriate rewards to minimize the Total Travel Delay (TTD).

Reinforcement Learning (RL)

Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning

2 code implementations27 May 2020 J. K. Terry, Nathaniel Grammel, Sanghyun Son, Benjamin Black, Aakriti Agrawal

Next, we formally introduce methods to extend parameter sharing to learning in heterogeneous observation and action spaces, and prove that these methods allow for convergence to optimal policies.

Multi-agent Reinforcement Learning reinforcement-learning +1

Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning

1 code implementation26 Feb 2020 Rohitkumar Arasanipalai, Aakriti Agrawal, Debasish Ghose

Quadcopters can suffer from loss of propellers in mid-flight, thus requiring a need to have a system that detects single and multiple propeller failures and an adaptive controller that stabilizes the propeller-deficient quadcopter.

Fault Detection reinforcement-learning +1

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