By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning.

Exploiting the short-sighted gradients should be balanced with adequate explorations.

We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training.

Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence.

Weproveconvergence for this method under standard assumptions and demonstrate empirically that it indeed enables lowerdiscount factors forapproximate reinforcement-learning methods.

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Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/