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Found 4 papers, 1 papers with code
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.
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)
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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.
