Convergence of Gradient Dynamics with a Variable Learning Rate

@inproceedings{Bowling2001ConvergenceOG,
  title={Convergence of Gradient Dynamics with a Variable Learning Rate},
  author={Michael H. Bowling and Manuela M. Veloso},
  booktitle={ICML},
  year={2001}
}
As multiagent environments become more prevalent we need to understand how this changes the agent-based paradigm. One aspect that is heavily affected by the presence of multiple agents is learning. Traditional learning algorithms have core assumptions, such as Markovian transitions, which are violated in these environments. Yet, understanding the behavior of learning algorithms in these domains is critical. Singh, Kearns, and Mansour (2000) examine gradient ascent learning, specifically within… CONTINUE READING
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