31,183 research outputs found
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation
The control of nonlinear dynamical systems remains a major challenge for
autonomous agents. Current trends in reinforcement learning (RL) focus on
complex representations of dynamics and policies, which have yielded impressive
results in solving a variety of hard control tasks. However, this new
sophistication and extremely over-parameterized models have come with the cost
of an overall reduction in our ability to interpret the resulting policies. In
this paper, we take inspiration from the control community and apply the
principles of hybrid switching systems in order to break down complex dynamics
into simpler components. We exploit the rich representational power of
probabilistic graphical models and derive an expectation-maximization (EM)
algorithm for learning a sequence model to capture the temporal structure of
the data and automatically decompose nonlinear dynamics into stochastic
switching linear dynamical systems. Moreover, we show how this framework of
switching models enables extracting hierarchies of Markovian and
auto-regressive locally linear controllers from nonlinear experts in an
imitation learning scenario.Comment: 2nd Annual Conference on Learning for Dynamics and Contro
Finding Competitive Network Architectures Within a Day Using UCT
The design of neural network architectures for a new data set is a laborious
task which requires human deep learning expertise. In order to make deep
learning available for a broader audience, automated methods for finding a
neural network architecture are vital. Recently proposed methods can already
achieve human expert level performances. However, these methods have run times
of months or even years of GPU computing time, ignoring hardware constraints as
faced by many researchers and companies. We propose the use of Monte Carlo
planning in combination with two different UCT (upper confidence bound applied
to trees) derivations to search for network architectures. We adapt the UCT
algorithm to the needs of network architecture search by proposing two ways of
sharing information between different branches of the search tree. In an
empirical study we are able to demonstrate that this method is able to find
competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day.
Extending the search time to five GPU days, we are able to outperform human
architectures and our competitors which consider the same types of layers
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Stick-Breaking Policy Learning in Dec-POMDPs
Expectation maximization (EM) has recently been shown to be an efficient
algorithm for learning finite-state controllers (FSCs) in large decentralized
POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often
converge to maxima that are far from optimal. This paper considers a
variable-size FSC to represent the local policy of each agent. These
variable-size FSCs are constructed using a stick-breaking prior, leading to a
new framework called \emph{decentralized stick-breaking policy representation}
(Dec-SBPR). This approach learns the controller parameters with a variational
Bayesian algorithm without having to assume that the Dec-POMDP model is
available. The performance of Dec-SBPR is demonstrated on several benchmark
problems, showing that the algorithm scales to large problems while
outperforming other state-of-the-art methods
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