14,992 research outputs found
Distributed Dictionary Learning
The paper studies distributed Dictionary Learning (DL) problems where the
learning task is distributed over a multi-agent network with time-varying
(nonsymmetric) connectivity. This formulation is relevant, for instance, in
big-data scenarios where massive amounts of data are collected/stored in
different spatial locations and it is unfeasible to aggregate and/or process
all the data in a fusion center, due to resource limitations, communication
overhead or privacy considerations. We develop a general distributed
algorithmic framework for the (nonconvex) DL problem and establish its
asymptotic convergence. The new method hinges on Successive Convex
Approximation (SCA) techniques coupled with i) a gradient tracking mechanism
instrumental to locally estimate the missing global information; and ii) a
consensus step, as a mechanism to distribute the computations among the agents.
To the best of our knowledge, this is the first distributed algorithm with
provable convergence for the DL problem and, more in general, bi-convex
optimization problems over (time-varying) directed graphs
Learning to Teach Reinforcement Learning Agents
In this article we study the transfer learning model of action advice under a
budget. We focus on reinforcement learning teachers providing action advice to
heterogeneous students playing the game of Pac-Man under a limited advice
budget. First, we examine several critical factors affecting advice quality in
this setting, such as the average performance of the teacher, its variance and
the importance of reward discounting in advising. The experiments show the
non-trivial importance of the coefficient of variation (CV) as a statistic for
choosing policies that generate advice. The CV statistic relates variance to
the corresponding mean. Second, the article studies policy learning for
distributing advice under a budget. Whereas most methods in the relevant
literature rely on heuristics for advice distribution we formulate the problem
as a learning one and propose a novel RL algorithm capable of learning when to
advise, adapting to the student and the task at hand. Furthermore, we argue
that learning to advise under a budget is an instance of a more generic
learning problem: Constrained Exploitation Reinforcement Learning
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