11/30/2023 0 Comments Relative entropyWe compare our method with state of art black-box optimization methods on standard uni-modal and multi-modal optimization functions, on simulated planar robot tasks and a complex robot ball throwing task.The proposed method considerably outperforms the existing approaches. Additionally the new method is able to sustain the exploration of the search distribution to avoid premature convergence. Instead, we use information theoretic constraints to bound the `distance' between the new and old data distribution while maximizing the objective function. The algorithm can be misled by an inaccurate optimum introduced by the surrogate. As the quality of such a quadratic approximation is limited, we do not greedily exploit the learned models. We learn simple, quadratic surrogate models of the objective function. Relative Entropy and Mutual Information in Gaussian Statistical Field Theory. time appears to slow down relative to a stationary observer. To alleviate these problems, we introduce a new surrogate-based stochastic search approach. It states that in a closed system, the total entropy (a measure of disorder or randomness). Yet, these algorithms require a lot of evaluations of the objective, scale poorly with the problem dimension, are affected by highly noisy objective functions and may converge prematurely. Due to their ease of use and their generality, they have recently also gained a lot of attention in operations research, machine learning and policy search. The default option for computing KL-divergence between discrete probability vectors would be. Applications to the relative entropy of entanglement in multipartite quantum systems are considered. Several basic corollaries of this criterion are presented. Stochastic search algorithms are general black-box optimizers. A criterion of local continuity of the relative entropy of resource - the relative entropy distance to the set of free states - is obtained. Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann Abstract These guarantees largely improve previously known results under much milder assumptions and cannot be significantly improved under general assumptions.Bibtex Metadata Paper Reviews SupplementalĪbbas Abdolmaleki, Rudolf Lioutikov, Jan R. For now, relative entropy can be thought of as a measure of discrepancy between two. We describe a variant of the recently proposed Relative Entropy Policy Search algorithm and show that its regret after $T$ episodes is $2\sqrt$ in the full information setting. Relative Entropy1 An measure of distance between probability distributions is relative entropy: D(p kq), X u2U p(u)log p(u) q(u) E log p(u) q(u) (23) Note that by property 3, the relative entropy is always greater than or equal to 0, with equality i q p. We assume that the learner is given access to a finite action space $\A$ and the state space $\X$ has a layered structure with $L$ layers, so that state transitions are only possible between consecutive layers. In this case, for two probability measures Q P, D(QkP) Z log dQ dP dQ. The relative entropy was also widely used in the case that µ is a probability measure. The natural performance measure in this learning problem is the regret defined as the difference between the total loss of the best stationary policy and the total loss suffered by the learner. When µ is the Lebesgue measure on an Eu-clidean space, h(X) D(Qkµ) is the dierential entropy of X Q. We study the problem of online learning in finite episodic Markov decision processes where the loss function is allowed to change between episodes. Bibtex Metadata Paper Reviews Supplemental
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