41 citations · 139 across the 36 of their papers we have counts for
9 papers · 1 filter
Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning
Akshat Agarwal, Abhinau Kumar, Kyle Dunovan +3
In the real world, agents often have to operate in situations with incomplete information, limited sensing capabilities, and inherently stochastic environments, making individual o…
Object-sensitive Deep Reinforcement Learning
Yuezhang Li, Katia Sycara, Rahul Iyer
Deep reinforcement learning has become popular over recent years, showing superiority on different visual-input tasks such as playing Atari games and robot navigation. Although obj…
Transparency and Explanation in Deep Reinforcement Learning Neural Networks
Rahul Iyer, Yuezhang Li, Huao Li +3
Autonomous AI systems will be entering human society in the near future to provide services and work alongside humans. For those systems to be accepted and trusted, the users shoul…
Challenges of Context and Time in Reinforcement Learning: Introducing Space Fortress as a Benchmark
Akshat Agarwal, Ryan Hope, Katia Sycara
Research in deep reinforcement learning (RL) has coalesced around improving performance on benchmarks like the Arcade Learning Environment. However, these benchmarks conspicuously…
Community Regularization of Visually-Grounded Dialog
Akshat Agarwal, Swaminathan Gurumurthy, Vasu Sharma +2
The task of conducting visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several…
Towards Better Interpretability in Deep Q-Networks
Raghuram Mandyam Annasamy, Katia Sycara
Deep reinforcement learning techniques have demonstrated superior performance in a wide variety of environments. As improvements in training algorithms continue at a brisk pace, th…