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9 papers · 1 filter
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang, Rein Houthooft, Davis Foote +6
Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov d…
Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners
Jakub Pachocki, Liam Roditty, Aaron Sidford +2
The girth of a graph, i.e. the length of its shortest cycle, is a fundamental graph parameter. Unfortunately all known algorithms for computing, even approximately, the girth and g…
Potential for optimizing Higgs boson CP measurement in H to tau tau decay at LHC and ML techniques
R. Józefowicz, E. Richter-Was, Z. Was
We investigate potential for measuring CP state of the Higgs boson in the H to tau tau$ decay with consecutive tau-lepton decays in channels: tau^+- to rho^+- nu_tau and tau^+- to…
Deep Learning with Differential Privacy
Martín Abadi, Andy Chu, Ian Goodfellow +4
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative dat…
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen, Yan Duan, Rein Houthooft +3
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervis…
Generative Adversarial Imitation Learning
Jonathan Ho, Stefano Ermon
Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost functio…