66 citations · 127 across the 6 of their papers we have counts for
6 papers
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…
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…
Evolution-Guided Policy Gradient in Reinforcement Learning
Shauharda Khadka, Kagan Tumer
Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically suffer from three core difficu…