text: 471,0,1282746555963854849,4633095775,Ravi Tej Akella,"1/ Exciting news! Introducing two new policy gradient methods: (i) Deep Bayesian Quadrature Policy Gradient (DBQPG), and (ii) Uncertainty Aware Policy Gradient (UAPG). Joint work with @kazizzad, Mohammad Ghavamzadeh, @AnimaAnandkumar, and @yisongyue Paper: <LINK> 2/ While there exist numerous policy gradient (PG) algorithms, most of them use the same PG estimator: Monte-Carlo (MC) method. In DBQPG, we replace MC with Bayesian quadrature (BQ) for estimating the PG. Project Link: <LINK> Code: <LINK> 3/ In comparison to MC, DBQPG provides (i) more accurate gradient estimates with a significantly lower variance, (ii) consistent improvement in the sample complexity and average return, and (iii) the uncertainty in policy gradient estimation. 4/ Further, we propose a new policy gradient method, UAPG, that uses the estimation uncertainty of the policy gradient to compute reliable policy updates with robust step-sizes.",https://arxiv.org/abs/2006.15637,"We study the problem of obtaining accurate policy gradient estimates using a finite number of samples. Monte-Carlo methods have been the default choice for policy gradient estimation, despite suffering from high variance in the gradient estimates. On the other hand, more sample efficient alternatives like Bayesian quadrature methods have received little attention due to their high computational complexity. In this work, we propose deep Bayesian quadrature policy gradient (DBQPG), a computationally efficient high-dimensional generalization of Bayesian quadrature, for policy gradient estimation. We show that DBQPG can substitute Monte-Carlo estimation in policy gradient methods, and demonstrate its effectiveness on a set of continuous control benchmarks. In comparison to Monte-Carlo estimation, DBQPG provides (i) more accurate gradient estimates with a significantly lower variance, (ii) a consistent improvement in the sample complexity and average return for several deep policy gradient algorithms, and, (iii) the uncertainty in gradient estimation that can be incorporated to further improve the performance. ",Deep Bayesian Quadrature Policy Optimization,4,"['1/ Exciting news! Introducing two new policy gradient methods: (i) Deep Bayesian Quadrature Policy Gradient (DBQPG), and (ii) Uncertainty Aware Policy Gradient (UAPG). Joint work with @kazizzad, Mohammad Ghavamzadeh, @AnimaAnandkumar, and @yisongyue\nPaper: <LINK>', '2/ While there exist numerous policy gradient (PG) algorithms, most of them use the same PG estimator: Monte-Carlo (MC) method. In DBQPG, we replace MC with Bayesian quadrature (BQ) for estimating the PG.\nProject Link: https://t.co/OyJ5eDihge\nCode: https://t.co/M2GBDQ2jYO', '3/ In comparison to MC, DBQPG provides (i) more accurate gradient estimates with a significantly lower variance, (ii) consistent improvement in the sample complexity and average return, and (iii) the uncertainty in policy gradient estimation.', '4/ Further, we propose a new policy gradient method, UAPG, that uses the estimation uncertainty of the policy gradient to compute reliable policy updates with robust step-sizes.']",20,06,923