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Found 9 papers, 4 papers with code
Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems
The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges.
Data-Driven Goal Recognition Design for General Behavioral Agents
Following existing literature, we use worst-case distinctiveness ($\textit{wcd}$) as a measure of the difficulty in inferring the goal of an agent in a decision-making environment.
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training.
Value of Assistance for Grasping
In multiple realistic settings, a robot is tasked with grasping an object without knowing its exact pose and relies on a probabilistic estimation of the pose to decide how to attempt the grasp.
Value of Assistance for Mobile Agents
In some settings, it may be possible to perform assistive actions that reduce uncertainty about a robot's location.
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes.
Explainable Reinforcement Learning via Model Transforms
This has given rise to a variety of approaches to explainability in RL that aim to reconcile discrepancies that may arise between the behavior of an agent and the behavior that is anticipated by an observer.
Collaboration Promotes Group Resilience in Multi-Agent AI
AI agents need to be robust to unexpected changes in their environment in order to safely operate in real-world scenarios.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Designing Environments Conducive to Interpretable Robot Behavior
Given structured environments (like warehouses and restaurants), it may be possible to design the environment so as to boost the interpretability of the robot's behavior or to shape the human's expectations of the robot's behavior.
