Deep Residual Reinforcement Learning

We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark. Moreover, we find the residual algorithm an effective approach to the distribution mismatch problem in model-based planning. Compared with the existing TD($k$) method, our residual-based method makes weaker assumptions about the model and yields a greater performance boost.Comment: AAMAS 202

Limiting SUSY compressed spectra scenarios

Typical searches for supersymmetry cannot test models in which the two lightest particles have a small ("compressed") mass splitting, due to the small momentum of the particles produced in the decay of the second-to-lightest particle. However, datasets with large missing transverse momentum ($E_{\rm T}^{\rm miss}$) can generically search for invisible particle production and therefore provide constraints on such models. We apply data from the ATLAS mono-jet (jet+$E_{\rm T}^{\rm miss}$) and vector-boson-fusion (forward jets and $E_{\rm T}^{\rm miss}$) searches to such models. The two datasets have complementary sensitivity, but in all cases experimental limits are at least five times weaker than theoretical predictions

Tevatron Discovery Potential for Fourth Generation Neutrinos: Dirac, Majorana and Everything in Between

We analyze the power of the Tevatron dataset to exclude or discover fourth generation neutrinos. In a general framework, one can have mixed left- and right-handed neutrinos, with Dirac and Majorana neutrinos as extreme cases. We demonstrate that a single Tevatron experiment can make powerful statements across the entire mixing space, extending LEP's mass limits of 60-80 GeV up to 150-175 GeV, depending on the mixing.Comment: 4 pages, pdflate