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Found 4 papers, 0 papers with code
Adaptive Smoothing Path Integral Control
We identify PICE as the infinite smoothing limit of such technique and show that the sample efficiency problems that PICE suffers disappear for finite levels of smoothing.
On the role of synaptic stochasticity in training low-precision neural networks
Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes.
Speedy Q-Learning
We introduce a new convergent variant of Q-learning, called speedy Q-learning, to address the problem of slow convergence in the standard form of the Q-learning algorithm.
Bounds on marginal probability distributions
We propose a novel bound on single-variable marginal probability distributions in factor graphs with discrete variables.
