9,785 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
Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fall with Grace
Despite impressive results using reinforcement learning to solve complex
problems from scratch, in robotics this has still been largely limited to
model-based learning with very informative reward functions. One of the major
challenges is that the reward landscape often has large patches with no
gradient, making it difficult to sample gradients effectively. We show here
that the robot state-initialization can have a more important effect on the
reward landscape than is generally expected. In particular, we show the
counter-intuitive benefit of including initializations that are unviable, in
other words initializing in states that are doomed to fail.Comment: Proceedings of the 2018 IEEE International Conference on SImulation,
Modeling and Programming for Autonomous Robots (SIMPAR), Brisbane, Australia,
16-19 201
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