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Found 11 papers, 6 papers with code
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent.
Learning Models of Adversarial Agent Behavior under Partial Observability
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction.
Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics.
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
On the other hand, expert performance degrades with the addition of xAI-based support ($p<0. 05$), indicating that the cost of paying attention to the xAI outweighs the benefits obtained from being provided additional information to enhance SA.
Learning Interpretable, High-Performing Policies for Autonomous Driving
While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD).
Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration
Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform novel tasks by providing demonstrations.
Learning from Suboptimal Demonstration via Self-Supervised Reward Regression
Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration.
Heterogeneous Learning from Demonstration
This inference requires the robot to be able to detect and classify the heterogeneity of its partners.
Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation
On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obtained human demonstrations.
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints.
Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration
For assistive robots and virtual agents to achieve ubiquity, machines will need to anticipate the needs of their human counterparts.
