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Found 12 papers, 1 papers with code
A Systems Theoretic Approach to Online Machine Learning
The framework is formulated in terms of input-output systems and is further divided into system structure and system behavior.
On Extending the Automatic Test Markup Language (ATML) for Machine Learning
This paper addresses the urgent need for messaging standards in the operational test and evaluation (T&E) of machine learning (ML) applications, particularly in edge ML applications embedded in systems like robots, satellites, and unmanned vehicles.
Inverse Reinforcement Learning for Strategy Identification
In this paper, the recovered reward are visually displayed, clustered using unsupervised learning, and classified using a supervised learner.
A Systems Theory of Transfer Learning
We interpret existing frameworks in terms of ours and go beyond existing frameworks to define notions of transferability, transfer roughness, and transfer distance.
Empirically Measuring Transfer Distance for System Design and Operation
We consider the use of transfer distance in the design of machine rebuild procedures to allow for transferable prognostic models.
Value-Decomposition Multi-Agent Actor-Critics
To obtain a reasonable trade-off between training efficiency and algorithm performance, we extend value-decomposition to actor-critics that are compatible with A2C and propose a novel actor-critic framework, value-decomposition actor-critics (VDACs).
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+2
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication
The flexibility of the graph structure enables our method to be applicable to a variety of multi-agent systems, e. g. dynamic systems that consist of varying numbers of agents and static systems with a fixed number of agents.
Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration
The ability to model and predict ego-vehicle's surrounding traffic is crucial for autonomous pilots and intelligent driver-assistance systems.
Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games
Problem uNE-MIRL is similar to uCE-MIRL in total game value maximization, but it is assumed that the agents are playing a Nash equilibrium.
Comparison of Multi-agent and Single-agent Inverse Learning on a Simulated Soccer Example
Comparison between IRL and MIRL is made in the context of an abstract soccer game, using both a game model in which the reward depends only on state and one in which it depends on both state and action.
Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games
The focus of this paper is a Bayesian framework for solving a class of problems termed multi-agent inverse reinforcement learning (MIRL).
Classroom Video Assessment and Retrieval via Multiple Instance Learning
We propose a multiple instance learning approach to content-based retrieval of classroom video for the purpose of supporting human assessing the learning environment.
