39,636 research outputs found
On-line support vector machines for function approximation
This paper describes an on-line method for building epsilon-insensitive support vector machines for regression as described in (Vapnik, 1995). The method is an extension of the method developed by (Cauwenberghs & Poggio, 2000) for building incremental support vector machines for classification. Machines obtained by using this approach are equivalent to the ones obtained by applying exact methods like quadratic programming, but they are obtained more quickly and allow the incremental addition of new points, removal of existing points and update of target values for existing data. This development opens the application of SVM regression to areas such as on-line prediction of temporal series or generalization of value functions in reinforcement learning.Postprint (published version
Riemannian game dynamics
We study a class of evolutionary game dynamics defined by balancing a gain
determined by the game's payoffs against a cost of motion that captures the
difficulty with which the population moves between states. Costs of motion are
represented by a Riemannian metric, i.e., a state-dependent inner product on
the set of population states. The replicator dynamics and the (Euclidean)
projection dynamics are the archetypal examples of the class we study. Like
these representative dynamics, all Riemannian game dynamics satisfy certain
basic desiderata, including positive correlation and global convergence in
potential games. Moreover, when the underlying Riemannian metric satisfies a
Hessian integrability condition, the resulting dynamics preserve many further
properties of the replicator and projection dynamics. We examine the close
connections between Hessian game dynamics and reinforcement learning in normal
form games, extending and elucidating a well-known link between the replicator
dynamics and exponential reinforcement learning.Comment: 47 pages, 12 figures; added figures and further simplified the
derivation of the dynamic
Neo: A Learned Query Optimizer
Query optimization is one of the most challenging problems in database
systems. Despite the progress made over the past decades, query optimizers
remain extremely complex components that require a great deal of hand-tuning
for specific workloads and datasets. Motivated by this shortcoming and inspired
by recent advances in applying machine learning to data management challenges,
we introduce Neo (Neural Optimizer), a novel learning-based query optimizer
that relies on deep neural networks to generate query executions plans. Neo
bootstraps its query optimization model from existing optimizers and continues
to learn from incoming queries, building upon its successes and learning from
its failures. Furthermore, Neo naturally adapts to underlying data patterns and
is robust to estimation errors. Experimental results demonstrate that Neo, even
when bootstrapped from a simple optimizer like PostgreSQL, can learn a model
that offers similar performance to state-of-the-art commercial optimizers, and
in some cases even surpass them
Efficient collective swimming by harnessing vortices through deep reinforcement learning
Fish in schooling formations navigate complex flow-fields replete with
mechanical energy in the vortex wakes of their companions. Their schooling
behaviour has been associated with evolutionary advantages including collective
energy savings. How fish harvest energy from their complex fluid environment
and the underlying physical mechanisms governing energy-extraction during
collective swimming, is still unknown. Here we show that fish can improve their
sustained propulsive efficiency by actively following, and judiciously
intercepting, vortices in the wake of other swimmers. This swimming strategy
leads to collective energy-savings and is revealed through the first ever
combination of deep reinforcement learning with high-fidelity flow simulations.
We find that a `smart-swimmer' can adapt its position and body deformation to
synchronise with the momentum of the oncoming vortices, improving its average
swimming-efficiency at no cost to the leader. The results show that fish may
harvest energy deposited in vortices produced by their peers, and support the
conjecture that swimming in formation is energetically advantageous. Moreover,
this study demonstrates that deep reinforcement learning can produce navigation
algorithms for complex flow-fields, with promising implications for energy
savings in autonomous robotic swarms.Comment: 26 pages, 14 figure
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