41 citations · 188 across the 52 of their papers we have counts for
25 papers · 1 filter
Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning
Ini Oguntola, Joseph Campbell, Simon Stepputtis +1
The ability to model the mental states of others is crucial to human social intelligence, and can offer similar benefits to artificial agents with respect to the social dynamics in…
Introspective Action Advising for Interpretable Transfer Learning
Joseph Campbell, Yue Guo, Fiona Xie +2
Transfer learning can be applied in deep reinforcement learning to accelerate the training of a policy in a target task by transferring knowledge from a policy learned in a related…
Characterizing Out-of-Distribution Error via Optimal Transport
Yuzhe Lu, Yilong Qin, Runtian Zhai +7
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models, so methods of predicting a model's performance on OOD data without labels are important…
On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning
Seth Karten, Siva Kailas, Huao Li +1
Explicit communication among humans is key to coordinating and learning. Social learning, which uses cues from experts, can greatly benefit from the usage of explicit communication…
Concept Learning for Interpretable Multi-Agent Reinforcement Learning
Renos Zabounidis, Joseph Campbell, Simon Stepputtis +2
Multi-agent robotic systems are increasingly operating in real-world environments in close proximity to humans, yet are largely controlled by policy models with inscrutable deep ne…
Predicting Out-of-Distribution Error with Confidence Optimal Transport
Yuzhe Lu, Zhenlin Wang, Runtian Zhai +3
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops. Being able to estimate…
Towards True Lossless Sparse Communication in Multi-Agent Systems
Seth Karten, Mycal Tucker, Siva Kailas +1
Communication enables agents to cooperate to achieve their goals. Learning when to communicate, i.e., sparse (in time) communication, and whom to message is particularly important…
ARC - Actor Residual Critic for Adversarial Imitation Learning
Ankur Deka, Changliu Liu, Katia Sycara
Adversarial Imitation Learning (AIL) is a class of popular state-of-the-art Imitation Learning algorithms commonly used in robotics. In AIL, an artificial adversary's misclassifica…
Probe-Based Interventions for Modifying Agent Behavior
Mycal Tucker, William Kuhl, Khizer Shahid +3
Neural nets are powerful function approximators, but the behavior of a given neural net, once trained, cannot be easily modified. We wish, however, for people to be able to influen…
Interpretable Learned Emergent Communication for Human-Agent Teams
Seth Karten, Mycal Tucker, Huao Li +3
Learning interpretable communication is essential for multi-agent and human-agent teams (HATs). In multi-agent reinforcement learning for partially-observable environments, agents…
Emergent Discrete Communication in Semantic Spaces
Mycal Tucker, Huao Li, Siddharth Agrawal +4
Neural agents trained in reinforcement learning settings can learn to communicate among themselves via discrete tokens, accomplishing as a team what agents would be unable to do al…
Deep Interpretable Models of Theory of Mind
Ini Oguntola, Dana Hughes, Katia Sycara
When developing AI systems that interact with humans, it is essential to design both a system that can understand humans, and a system that humans can understand. Most deep network…
Predicting Human Strategies in Simulated Search and Rescue Task
Vidhi Jain, Rohit Jena, Huao Li +4
In a search and rescue scenario, rescuers may have different knowledge of the environment and strategies for exploration. Understanding what is inside a rescuer's mind will enable…
Addressing reward bias in Adversarial Imitation Learning with neutral reward functions
Rohit Jena, Siddharth Agrawal, Katia Sycara
Generative Adversarial Imitation Learning suffers from the fundamental problem of reward bias stemming from the choice of reward functions used in the algorithm. Different types of…
Augmenting GAIL with BC for sample efficient imitation learning
Rohit Jena, Changliu Liu, Katia Sycara
Imitation learning is the problem of recovering an expert policy without access to a reward signal. Behavior cloning and GAIL are two widely used methods for performing imitation l…
Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds
Rahul Radhakrishnan Iyer, Ronghuo Zheng, Yuezhang Li +1
Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has…
MAME : Model-Agnostic Meta-Exploration
Swaminathan Gurumurthy, Sumit Kumar, Katia Sycara
Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples. Developing efficient ex…
Learning Transferable Cooperative Behavior in Multi-Agent Teams
Akshat Agarwal, Sumit Kumar, Katia Sycara
While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity gra…
Active Learning with Gaussian Processes for High Throughput Phenotyping
Sumit Kumar, Wenhao Luo, George Kantor +1
A looming question that must be solved before robotic plant phenotyping capabilities can have significant impact to crop improvement programs is scalability. High Throughput Phenot…
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…
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…