Multi-Player Residual Advantage Learning With General Function Approximation

@inproceedings{Harmon1996MultiPlayerRA,
  title={Multi-Player Residual Advantage Learning With General Function Approximation},
  author={Mance E. Harmon},
  year={1996}
}
A new algorithm, advantage learning, is presented that improves on advantage updating by requiring that a single function be learned rather than two. Furthermore, advantage learning requires only a single type of update, the learning update, while advantage updating requires two different types of updates, a learning update and a normilization update. The reinforcement learning system uses the residual form of advantage learning. An application of reinforcement learning to a Markov game is… CONTINUE READING
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