A recent line of work has established uncoupled learning dynamics such that, when employed by all players in a game, each player's regret after T repetitions grows polylogarithmically in T, an exponential improvement over the traditional guarantees within the no-regret framework. However, so far these results have only been limited to certain classes of games with structured strategy spaces--such as normal-form and extensive-form games. The question as to whether O(polylogT) regret bounds can be obtained for general convex and compact strategy sets--which occur in many fundamental models in economics and multiagent systems--while retaining efficient strategy updates is an importantquestion.

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

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

Human teammates often form conscious andsubconscious expectations ofeach other during interaction. Teaming success is contingent on whether such expectations can be met. Similarly,for an intelligent agent tooperate beside ahuman, it must consider the human's expectation of its behavior. Disregarding such expectations can lead to the loss of trust and degraded team performance. A key challenge here is that the human's expectation may not align with the agent's optimal behavior,e.g., duetothehuman'spartial orinaccurate understanding of thetaskdomain.

Thoughunbiased, IScanbehigh-variance. Alowervariancealternativeis Weighted IS (WIS). Figure 4: Learning Ratesensitivityplotsinthe Random Walk Markov Chain, withbuffersizen = 15000 andmini-batchsizek = 16.

TrueSkill can handle multi-player games.

Figure 7: Learningcurvesof ATOC (left) and CommNet (right) during learningonpredator -prey.

We consider a scenario where multiple agents are learning a common decision vector from datawhich canbeinfluenced bytheagents' decisions.