Corpus ID: 212633838

FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis

@inproceedings{Sinha2020FormulaZeroDR,
  title={FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis},
  author={Aman Sinha and Matthew O'Kelly and Hongrui Zheng and Rahul Mangharam and John C. Duchi and Russ Tedrake},
  booktitle={ICML},
  year={2020}
}
Balancing performance and safety is crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. This work makes algorithmic contributions to both challenges. First, to generate a realistic, diverse set of opponents, we… Expand
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References

SHOWING 1-10 OF 115 REFERENCES
Variational Inference with Normalizing Flows
TLDR
It is demonstrated that the theoretical advantages of having posteriors that better match the true posterior, combined with the scalability of amortized variational approaches, provides a clear improvement in performance and applicability of variational inference. Expand
Improved Variational Inference with Inverse Autoregressive Flow
TLDR
A new type of normalizing flow, inverse autoregressive flow (IAF), is proposed that, in contrast to earlier published flows, scales well to high-dimensional latent spaces and significantly improves upon diagonal Gaussian approximate posteriors. Expand
Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm
We present a Monte Carlo algorithm to find approximate solutions of the traveling salesman problem. The algorithm generates randomly the permutations of the stations of the traveling salesman trip,Expand
The Nonstochastic Multiarmed Bandit Problem
TLDR
A solution to the bandit problem in which an adversary, rather than a well-behaved stochastic process, has complete control over the payoffs. Expand
Hit-and-run mixes fast
  • L. Lovász
  • Mathematics, Computer Science
  • Math. Program.
  • 1999
TLDR
It is shown that the “hit-and-run” algorithm for sampling from a convex body K mixes in time O*(n2R2/r2), where R and r are the radii of the inscribed and circumscribed balls of K and the bound is best possible in terms of R,r and n. Expand
Hit-and-Run Algorithms for Generating Multivariate Distributions
We introduce a general class of Hit-and-Run algorithms for generating essentially arbitrary absolutely continuous distributions on Rd. They include the Hypersphere Directions algorithm and theExpand
Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions
TLDR
This work considers the Monte Carlo problem of generating points uniformly distributed within an arbitrary bounded measurable region and considers the class of Markovian methods considered, which are potentially superior to conventional rejection techniques for large dimensional regions. Expand
A Noncooperative Game Approach to Autonomous Racing
  • A. Liniger, J. Lygeros
  • Computer Science, Mathematics
  • IEEE Transactions on Control Systems Technology
  • 2020
TLDR
The simulation study shows that the presented games can successfully model different racing behaviors and generate interesting racing situations and two methods for guaranteeing feasibility of the resulting coupled repeated games are studied. Expand
Adversarial Policies: Attacking Deep Reinforcement Learning
TLDR
The existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents is demonstrated. Expand
AlphaStar: an evolutionary computation perspective
TLDR
This paper analyzes AlphaStar primarily through the lens of EC, presenting a new look at the system and relating it to many concepts in the field, and hopes to provide a bridge between the wider EC community and one of the most significant AI systems developed in recent times. Expand
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