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0111 · Mar 201619922001200920172026
2 results for Foster-Lyapunov

Policy gradient algorithm with variable learning rates achieves near-optimal performance in multi-arm bandit problems.

problem Optimizing a policy gradient algorithm for multi-arm bandit problems with variable learning rates.
method Applied Foster-Lyapunov techniques to analyze a Markov chain formed by the state of the algorithm.
result The policy gradient algorithm converges to the optimal arm with logarithmic or poly-logarithmic regret.

The Contrastive Divergence (CD) algorithm has achieved notable success in training energy-based models including Restricted Boltzmann Machines and played a key role in the emergence of deep learning. The idea of this algorithm is to approximate the intractable term in the exact gradient of the log-likelihood function b…

2016-03-17abs ↗pdf ↗