A pragmatic look at deep imitation learning

The introduction of the generative adversarial imitation learning (GAIL) algorithm has spurred the development of scalable imitation learning approaches using deep neural networks. The GAIL objective can be thought of as 1) matching the expert policy's state distribution; 2) penalising the learned policy's state distribution; and 3) maximising entropy. While theoretically motivated, in practice GAIL can be difficult to apply, not least due to the instabilities of adversarial training. In this paper, we take a pragmatic look at GAIL and related imitation learning algorithms. We implement and automatically tune a range of algorithms in a unified experimental setup, presenting a fair evaluation between the competing methods. From our results, our primary recommendation is to consider non-adversarial methods. Furthermore, we discuss the common components of imitation learning objectives, and present promising avenues for future research

Neurotoxic Vulnerability Underlying Schizophrenia Spectrum Disorders

Neurotoxic vulnerability that putatively contributes to the etiopathogenesis of schizophrenia spectrum disorders encompasses perinatal adversity, genetic linkage, epigenetic disadvantage and neurodegenerative propensities that affect both symptom domains, positive, negative and cognitive, and biomarkers of the disorder. Molecular and cellular apoptosis/excitotoxicity that culminates in regional brain loss, reductions in reelin expression, trophic disruption, perinatal adversity, glycogen synthase kinase-3 dysregulation and various instances of oxidative stress all influence the final end-point disorder. The existence of prodromal psychotic phases, structural-functional aspects of regional neuroimaging, dopamine signal overexpression and psychosis propensity provide substance for neurodegenerative influences. The pathophysiology of schizophrenia spectrum disorder encompasses the destruction of normal functioning of the neurotrophins, in particular brain-derived neurotrophic factor (BDNF), dyskinesia of necessary movements, and metabolic-metabolomic and proteomic markers. Neurotoxic accidents combined with genetic susceptibility appears to play a role in interfering normal neurodevelopment or in the tissue-destructive neurodegeneration or both, thereby elevating the eventual risk for disorder tendencies and eventual expression