1,154 research outputs found
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
We consider the problem of multiple agents sensing and acting in environments
with the goal of maximising their shared utility. In these environments, agents
must learn communication protocols in order to share information that is needed
to solve the tasks. By embracing deep neural networks, we are able to
demonstrate end-to-end learning of protocols in complex environments inspired
by communication riddles and multi-agent computer vision problems with partial
observability. We propose two approaches for learning in these domains:
Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning
(DIAL). The former uses deep Q-learning, while the latter exploits the fact
that, during learning, agents can backpropagate error derivatives through
(noisy) communication channels. Hence, this approach uses centralised learning
but decentralised execution. Our experiments introduce new environments for
studying the learning of communication protocols and present a set of
engineering innovations that are essential for success in these domains
Fast Context Adaptation via Meta-Learning
We propose CAVIA for meta-learning, a simple extension to MAML that is less
prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA
partitions the model parameters into two parts: context parameters that serve
as additional input to the model and are adapted on individual tasks, and
shared parameters that are meta-trained and shared across tasks. At test time,
only the context parameters are updated, leading to a low-dimensional task
representation. We show empirically that CAVIA outperforms MAML for regression,
classification, and reinforcement learning. Our experiments also highlight
weaknesses in current benchmarks, in that the amount of adaptation needed in
some cases is small.Comment: Published at the International Conference on Machine Learning (ICML)
201
Atom in a coherently controlled squeezed vacuum
A broadband squeezed vacuum photon field is characterized by a complex
squeezing function. We show that by controlling the wavelength dependence of
its phase it is possible to change the dynamics of the atomic polarization
interacting with the squeezed vacuum. Such a phase modulation effectively
produces a finite range temporal interaction kernel between the two quadratures
of the atomic polarization yielding the change in the decay rates as well as
the appearance of additional oscillation frequencies. We show that decay rates
slower than the spontaneous decay rate can be achieved even for a squeezed bath
in the classic regime. For linear and quadratic phase modulations the power
spectrum of the scattered light exhibits narrowing of the central peak due to
the modified decay rates. For strong phase modulations side lobes appear
symmetrically around the central peak reflecting additional oscillation
frequencies.Comment: 4 pages, 4 figure
Impact of Pedometer Use and Self-Regulation Strategies on Junior High School Physical Education Students\u27 Daily Step Counts
Background: The aim of this study was to determine the impact of pedometer use and self-regulation strategies on adolescents’ daily physical activity.
Methods: Junior high school students (n = 113) enrolled in seventh- and eighth-grade physical education classes (52 girls, 61 boys) volunteered to participate in a 5-week study to assess daily step counts. Ten physical education classes were randomly assigned to 1 of 3 groups: (a) self-regulation, (b) open, and (c) control.
Results: A repeated-measures, mixed-model analysis of variance revealed a significant 3 × 4 (Group by Time) interaction effect, F6,290 = 2.64, P \u3c .02. Followup analyses indicated participants in the self-regulation group took 2071 to 4141 more steps/d than the control. No other significant differences emerged among groups on step counts.
Conclusions: It appears that having access to and charting daily step counts (ie, self-regulatory strategies) positively influenced young adolescents to attain a higher number of steps/d
The ultrafilter number for singular cardinals
We prove the consistency of a singular cardinal with small value of
the ultrafilter number , and arbitrarily large value of .Comment: 8 page
Using Big Bang Nucleosynthesis to Extend CMB Probes of Neutrino Physics
We present calculations showing that upcoming Cosmic Microwave Background
(CMB) experiments will have the power to improve on current constraints on
neutrino masses and provide new limits on neutrino degeneracy parameters. The
latter could surpass those derived from Big Bang Nucleosynthesis (BBN) and the
observationally-inferred primordial helium abundance. These conclusions derive
from our Monte Carlo Markov Chain (MCMC) simulations which incorporate a full
BBN nuclear reaction network. This provides a self-consistent treatment of the
helium abundance, the baryon number, the three individual neutrino degeneracy
parameters and other cosmological parameters. Our analysis focuses on the
effects of gravitational lensing on CMB constraints on neutrino rest mass and
degeneracy parameter. We find for the PLANCK experiment that total (summed)
neutrino mass eV could be ruled out at or better.
Likewise neutrino degeneracy parameters and could be detected or ruled out at
confidence, or better. For POLARBEAR we find that the corresponding detectable
values are , , and , while for EPIC we obtain ,
, and . Our forcast for
EPIC demonstrates that CMB observations have the potential to set constraints
on neutrino degeneracy parameters which are better than BBN-derived limits and
an order of magnitude better than current WMAP-derived limits.Comment: 27 pages, 11 figures, matches published version in JCA
Computing Convex Coverage Sets for Faster Multi-objective Coordination
In this article, we propose new algorithms for multi-objective coordination graphs (MO- CoGs). Key to the efficiency of these algorithms is that they compute a convex coverage set (CCS) instead of a Pareto coverage set (PCS). Not only is a CCS a sufficient solution set for a large class of problems, it also has important characteristics that facilitate more efficient solutions. We propose two main algorithms for computing a CCS in MO-CoGs. Convex multi-objective variable elimination (CMOVE) computes a CCS by performing a series of agent eliminations, which can be seen as solving a series of local multi-objective subproblems. Variable elimination linear support (VELS) iteratively identifies the single weight vector w that can lead to the maximal possible improvement on a partial CCS and calls variable elimination to solve a scalarized instance of the problem for w. VELS is faster than CMOVE for small and medium numbers of objectives and can compute an ε-approximate CCS in a fraction of the runtime. In addition, we propose variants of these methods that employ AND/OR tree search instead of variable elimination to achieve memory efficiency. We analyze the runtime and space complexities of these methods, prove their correctness, and compare them empirically against a naive baseline and an existing PCS method, both in terms of memory-usage and runtime. Our results show that, by focusing on the CCS, these methods achieve much better scalability in the number of agents than the current state of the art
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