Better Optimism By Bayes: Adaptive Planning with Rich Models

The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We ask whether it is feasible and truly beneficial to combine rich probabilistic models with a closer approximation to fully Bayesian planning. First, we use a collection of counterexamples to show formal problems with the over-optimism inherent in Thompson sampling. Then we leverage state-of-the-art techniques in efficient Bayes-adaptive planning and non-parametric Bayesian methods to perform qualitatively better than both existing conventional algorithms and Thompson sampling on two contextual bandit-like problems.Comment: 11 pages, 11 figure

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, finding the resulting Bayes-optimal policies is notoriously taxing, since the search space becomes enormous. In this paper we introduce a tractable, sample-based method for approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our approach outperformed prior Bayesian model-based RL algorithms by a significant margin on several well-known benchmark problems -- because it avoids expensive applications of Bayes rule within the search tree by lazily sampling models from the current beliefs. We illustrate the advantages of our approach by showing it working in an infinite state space domain which is qualitatively out of reach of almost all previous work in Bayesian exploration.Comment: 14 pages, 7 figures, includes supplementary material. Advances in Neural Information Processing Systems (NIPS) 201

Searching for Roots of Entrainment and Joint Action in Early Musical Interactions

When people play music and dance together, they engage in forms of musical joint action that are often characterized by a shared sense of rhythmic timing and affective state (i.e., temporal and affective entrainment). In order to understand the origins of musical joint action, we propose a model in which entrainment is linked to dual mechanisms (motor resonance and action simulation), which in turn support musical behavior (imitation and complementary joint action). To illustrate this model, we consider two generic forms of joint musical behavior: chorusing and turn-taking. We explore how these common behaviors can be founded on entrainment capacities established early in human development, specifically during musical interactions between infants and their caregivers. If the roots of entrainment are found in early musical interactions which are practiced from childhood into adulthood, then we propose that the rehearsal of advanced musical ensemble skills can be considered to be a refined, mimetic form of temporal and affective entrainment whose evolution begins in infancy

Minding the Gap: Appraising the promise and performance of regulatory reform in Australia

‘Mind the Gap!’ is an almost iconic exhortation, originating in the London Underground, warning travellers to be careful when navigating the ‘gap’ between the platform and train. In this volume, Peter Carroll, Rex Deighton-Smith, Helen Silver and Chris Walker retrospectively assess the ‘gap’ — no less dynamic and perilous in a public policy context — between the promise and performance of successive waves of regulation in Australia since the 1980s. Regulatory bodies exist to exercise what might be broadly termed ‘control functions’ and, by nature, tend to be conservative both in their culture and operations. Institutional conservatism does not, of necessity, preclude the exercise of creativity and foresight, both of which are sorely required if government is to successfully meet the challenge of delivering more effective and less costly regulation. The business and policy environment is complex, the risks are great and the rewards of success and the costs of failure will be enormous. The true measure of success will be how effectively we are able to close the gap between promise and performance

The Ursinus Weekly, October 8, 1970

Pettit inauguration scheduled; Appointments still undetermined • Ursinus institutes security measures to protect students • Obituaries: Dr. Paul R. Wagner; Nora Shuler Helfferich • Ashley Montagu appears in first Forum program • Editorial: Generation politics • Focus: Art Severance • Dr. Donald L. Helfferich: A zest for life • Kilt-klad\u27s komment • Diplomat aerials trip Bears in season debut • C C streak to nine; Albert sets record • Bears register second defeathttps://digitalcommons.ursinus.edu/weekly/1128/thumbnail.jp

The Ursinus Weekly, October 8, 1970

Pettit inauguration scheduled; Appointments still undetermined • Ursinus institutes security measures to protect students • Obituaries: Dr. Paul R. Wagner; Nora Shuler Helfferich • Ashley Montagu appears in first Forum program • Editorial: Generation politics • Focus: Art Severance • Dr. Donald L. Helfferich: A zest for life • Kilt-klad\u27s komment • Diplomat aerials trip Bears in season debut • C C streak to nine; Albert sets record • Bears register second defeathttps://digitalcommons.ursinus.edu/weekly/1128/thumbnail.jp

The Detection of Ionizing Radiation by Plasma Panel Sensors: Cosmic Muons, Ion Beams and Cancer Therapy

The plasma panel sensor is an ionizing photon and particle radiation detector derived from PDP technology with high gain and nanosecond response. Experimental results in detecting cosmic ray muons and beta particles from radioactive sources are described along with applications including high energy and nuclear physics, homeland security and cancer therapeuticsComment: Presented at SID Symposium, June 201