32 citations · 74 across the 16 of their papers we have counts for
8 papers · 1 filter
On Local Aggregation in Heterophilic Graphs
Hesham Mostafa, Marcel Nassar, Somdeb Majumdar
Many recent works have studied the performance of Graph Neural Networks (GNNs) in the context of graph homophily - a label-dependent measure of connectivity. Traditional GNNs gener…
Dream and Search to Control: Latent Space Planning for Continuous Control
Anurag Koul, Varun V. Kumar, Alan Fern +1
Learning and planning with latent space dynamics has been shown to be useful for sample efficiency in model-based reinforcement learning (MBRL) for discrete and continuous control…
Learning Intrinsic Symbolic Rewards in Reinforcement Learning
Hassam Sheikh, Shauharda Khadka, Santiago Miret +1
Learning effective policies for sparse objectives is a key challenge in Deep Reinforcement Learning (RL). A common approach is to design task-related dense rewards to improve task…
Safety Aware Reinforcement Learning (SARL)
Santiago Miret, Somdeb Majumdar, Carroll Wainwright
As reinforcement learning agents become increasingly integrated into complex, real-world environments, designing for safety becomes a critical consideration. We specifically focus…
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
Shauharda Khadka, Estelle Aflalo, Mattias Marder +6
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to memory hierarchies is crit…
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Shauharda Khadka, Somdeb Majumdar, Santiago Miret +2
Many cooperative multiagent reinforcement learning environments provide agents with a sparse team-based reward, as well as a dense agent-specific reward that incentivizes learning…
Collaborative Evolutionary Reinforcement Learning
Shauharda Khadka, Somdeb Majumdar, Tarek Nassar +5
Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically struggle with achieving effective ex…
Artificial Intelligence for Prosthetics - challenge solutions
Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty +47
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a giv…