Entropy-regularized Wasserstein distributionally robust shape and topology optimizationRobust optimizationDistributional robustnessWassertstein distanceEntropic regularizationShape optimizationTopology optimizationLinear elasticityThis brief note aims to introduce the recent paradigm of distributional robustness in the field...
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PRM选择:hard label、soft label 或者 entropy-regularized label? 发布于 2024-12-17 20:48・IP 属地浙江 赞同3 分享收藏 写下你的评论... 还没有评论,发表第一个评论吧登录知乎,您可以享受以下权益: 更懂你的优质内容 更专业的大咖答主 更深度的互动交流 更高效的创作环境立即登录/注册...
We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions. Two generative models are proposed. One uses EOT cost directly in an one-shot optimization problem and the other uses EOT cost iteratively in an adversarial game...
解决的问题经验回放的时候, buffer中的完成的goals经常biased towards行为策略。例子:训练智能体到达空间的某个地方。刚开始随机策略,一开始的轨迹在初始位置附近,轨迹分布是高斯分布。这不是uniform。从这样…
We present a new method for reconstructing two-dimensional mass maps of galaxy clusters from the image distortion of background galaxies. In contrast to most previous approaches, which directly convert locally averaged image ellipticities to mass maps (direct methods), our entropy-regularized maximum-...
(a) We propose a novel entropy regularized TRPO that adds Shannon entropy to the KL divergence constraint which prompts TRPO into better and wider explorations. To the best of our knowledge, this is the first TRPO variant to regularize its constraint to control the exploration directly; (b) ...
Our proof relies on the correspondence of the solutions of entropy-regularized Markov decision processes with gradient flows of the unregularized reward with respect to a Riemannian metric common in natural policy gradient methods. Further, this correspondence allows us to identify the limit of the ...
(A) Maximum Entropy Regularized RL 设想对于policy π ,它有一个 distribution of trajectories pπ , R(π) 是expected total reward, 我们希望maximize R(π)=R′(π)+τH(pπ) . 也就是说不仅仅希望expected reward最大, 还希望trajectory 更多样化. 注意到 H(pπ) 可以被拆分成 逐项相加, 如果envi...
Assuming access to exact policy evaluation, we demonstrate that the algorithm converges linearly -- or even quadratically once it enters a local region around the optimal policy -- when computing optimal value functions of the regularized MDP. Moreover, the algorithm is provably stable vis-à-vis...