Search Results - "Khadka, Shauharda"

Neuroevolution of a Modular Memory-Augmented Neural Network for Deep Memory Problems by Khadka, Shauharda, Chung, Jen Jen, Tumer, Kagan

“…We present Modular Memory Units (MMUs), a new class of memory-augmented neural network. MMU builds on the gated neural architecture of Gated Recurrent Units…”
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Evolution-Guided Policy Gradient in Reinforcement Learning by Khadka, Shauharda, Tumer, Kagan

“…Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically suffer…”
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Learning Intrinsic Symbolic Rewards in Reinforcement Learning by Sheikh, Hassam Ullah, Khadka, Shauharda, Miret, Santiago, Majumdar, Somdeb, Phielipp, Mariano

“…Learning effective policies for sparse objectives is a key challenge in Deep Reinforcement Learning (RL). A common approach is to design task-related dense…”
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Memory-Based Multiagent One-Shot Learning: Extended Abstract by Khadka, Shauharda, Yates, Connor, Tumer, Kagan

“…Learning correct behavior from one example (one-shot learning) is particularly difficult in multiagent systems where the pertinent information is potentially…”
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Learning Intrinsic Symbolic Rewards in Reinforcement Learning by Sheikh, Hassam, Khadka, Shauharda, Miret, Santiago, Majumdar, Somdeb

“…Learning effective policies for sparse objectives is a key challenge in Deep Reinforcement Learning (RL). A common approach is to design task-related dense…”
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Memory-augmented multi-robot teams that learn to adapt by Khadka, Shauharda, Chung, Jen Jen, Tumer, Kagan

“…As multi-robot teams increasingly permeate real world environments like factories, homes and other extrater-restrial surfaces, the ability to form joint…”
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Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination by Khadka, Shauharda, Majumdar, Somdeb, Miret, Santiago, McAleer, Stephen, Tumer, Kagan

“…Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020 Many cooperative multiagent reinforcement learning…”
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Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning by Khadka, Shauharda, Aflalo, Estelle, Marder, Mattias, Ben-David, Avrech, Miret, Santiago, Mannor, Shie, Hazan, Tamir, Tang, Hanlin, Majumdar, Somdeb

“…For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to…”
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Collaborative Evolutionary Reinforcement Learning by Khadka, Shauharda, Majumdar, Somdeb, Nassar, Tarek, Dwiel, Zach, Tumer, Evren, Miret, Santiago, Liu, Yinyin, Tumer, Kagan

“…Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019 Deep reinforcement learning algorithms have been…”
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Artificial Intelligence for Prosthetics - challenge solutions by Kidziński, Łukasz, Ong, Carmichael, Mohanty, Sharada Prasanna, Hicks, Jennifer, Carroll, Sean F, Zhou, Bo, Zeng, Hongsheng, Wang, Fan, Lian, Rongzhong, Tian, Hao, Jaśkowski, Wojciech, Andersen, Garrett, Lykkebø, Odd Rune, Toklu, Nihat Engin, Shyam, Pranav, Srivastava, Rupesh Kumar, Kolesnikov, Sergey, Hrinchuk, Oleksii, Pechenko, Anton, Ljungström, Mattias, Wang, Zhen, Hu, Xu, Hu, Zehong, Qiu, Minghui, Huang, Jun, Shpilman, Aleksei, Sosin, Ivan, Svidchenko, Oleg, Malysheva, Aleksandra, Kudenko, Daniel, Rane, Lance, Bhatt, Aditya, Wang, Zhengfei, Qi, Penghui, Yu, Zeyang, Peng, Peng, Yuan, Quan, Li, Wenxin, Tian, Yunsheng, Yang, Ruihan, Ma, Pingchuan, Khadka, Shauharda, Majumdar, Somdeb, Dwiel, Zach, Liu, Yinyin, Tumer, Evren, Watson, Jeremy, Salathé, Marcel, Levine, Sergey, Delp, Scott

“…In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a…”
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