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Found 3 papers, 3 papers with code
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games
Under a set of assumptions, we prove the convergence of the algorithm to a subjective notion of Markov perfect Nash equilibrium in NAMGs.
BioLCNet: Reward-modulated Locally Connected Spiking Neural Networks
Brain-inspired computation and information processing alongside compatibility with neuromorphic hardware have made spiking neural networks (SNN) a promising method for solving learning tasks in machine learning (ML).
Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework
A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used.
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