This paper investigates evaluation of learned multiagent strategies given noisy game outcomes.

These algorithms are typically employed in settings where the number of players and the type of interaction (competitive, cooperative, etc.) are both

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper introduces a theoretical framework which combines both dynamic and correlated topic models. The proposed approach is based on a latent factor model. The authors provide an interesting discussion on admixture models (traditional topic models) versus factor models. One of the main advantages of the chosen approach is the ability to model both positive topic usage and negative topic usage.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper considers the estimation of an unknown vector v0 from noisy quadratic observations and some additional information regarding v0. Specifically, it considers that the unknown vector v0 is from a convex cone. It rigorously shows that the resulting optimization problem is tractable. Note that the resulting optimization problem in Eq.(3) is non-convex.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper introduces a GP-Vol model to flexibly capture the time-dependent changes in variance, and develops a new online algorithm for fully Bayesian inference under the model. The paper is clearly written, the developed inference method seems technically sound, and the presented results look promising. My opinion on the model itself, using a non-parametric approach such as using the GP prior on the transition function (as in the paper), seems, though, a bit an obvious way of extending the prior work developed in the finance area. So, I wouldn't put too high grade on the paper in terms of its originality.

Search (PNS), are effective in playing and solving games. However, the understanding of their performance in industrial applications is still limited.

This paper studies a recent proposal to use randomized value functions to drive exploration in reinforcement learning.

Then, we have that 1. y Fact 2. Let z:= [ x; y ] and z This can be easily proven using the AM-GM inequality. Fact 3. Let z:= [ x; y ] R It is a crucial building block for the algorithms in this work. The following classical theorem holds for AGD. We will start by giving a precise statement of Algorithm 1.Algorithm 1 Alternating Best Response (ABR)Require: g (,), Initial point z The basic idea is the following. The following two lemmas about the inexact APP A algorithm follow from the proof of Theorem 4.1 [ Here we provide their proofs for completeness.