63,164 research outputs found
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
Microfluidic devices are utilized to control and direct flow behavior in a
wide variety of applications, particularly in medical diagnostics. A
particularly popular form of microfluidics -- called inertial microfluidic flow
sculpting -- involves placing a sequence of pillars to controllably deform an
initial flow field into a desired one. Inertial flow sculpting can be formally
defined as an inverse problem, where one identifies a sequence of pillars
(chosen, with replacement, from a finite set of pillars, each of which produce
a specific transformation) whose composite transformation results in a
user-defined desired transformation. Endemic to most such problems in
engineering, inverse problems are usually quite computationally intractable,
with most traditional approaches based on search and optimization strategies.
In this paper, we pose this inverse problem as a Reinforcement Learning (RL)
problem. We train a DoubleDQN agent to learn from this environment. The results
suggest that learning is possible using a DoubleDQN model with the success
frequency reaching 90% in 200,000 episodes and the rewards converging. While
most of the results are obtained by fixing a particular target flow shape to
simplify the learning problem, we later demonstrate how to transfer the
learning of an agent based on one target shape to another, i.e. from one design
to another and thus be useful for a generic design of a flow shape.Comment: Neurips 2018 Deep RL worksho
Visual Imitation Learning with Recurrent Siamese Networks
It would be desirable for a reinforcement learning (RL) based agent to learn
behaviour by merely watching a demonstration. However, defining rewards that
facilitate this goal within the RL paradigm remains a challenge. Here we
address this problem with Siamese networks, trained to compute distances
between observed behaviours and the agent's behaviours. Given a desired motion
such Siamese networks can be used to provide a reward signal to an RL agent via
the distance between the desired motion and the agent's motion. We experiment
with an RNN-based comparator model that can compute distances in space and time
between motion clips while training an RL policy to minimize this distance.
Through experimentation, we have had also found that the inclusion of
multi-task data and an additional image encoding loss helps enforce the
temporal consistency. These two components appear to balance reward for
matching a specific instance of behaviour versus that behaviour in general.
Furthermore, we focus here on a particularly challenging form of this problem
where only a single demonstration is provided for a given task -- the one-shot
learning setting. We demonstrate our approach on humanoid agents in both 2D
with degrees of freedom (DoF) and 3D with DoF.Comment: PrePrin
SuperSpike: Supervised learning in multi-layer spiking neural networks
A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico. Here we revisit the problem of supervised learning
in temporally coding multi-layer spiking neural networks. First, by using a
surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based
three factor learning rule capable of training multi-layer networks of
deterministic integrate-and-fire neurons to perform nonlinear computations on
spatiotemporal spike patterns. Second, inspired by recent results on feedback
alignment, we compare the performance of our learning rule under different
credit assignment strategies for propagating output errors to hidden units.
Specifically, we test uniform, symmetric and random feedback, finding that
simpler tasks can be solved with any type of feedback, while more complex tasks
require symmetric feedback. In summary, our results open the door to obtaining
a better scientific understanding of learning and computation in spiking neural
networks by advancing our ability to train them to solve nonlinear problems
involving transformations between different spatiotemporal spike-time patterns
Capture, Learning, and Synthesis of 3D Speaking Styles
Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60 fps and synchronized audio from 12 speakers. We then train a
neural network on our dataset that factors identity from facial motion. The
learned model, VOCA (Voice Operated Character Animation) takes any speech
signal as input - even speech in languages other than English - and
realistically animates a wide range of adult faces. Conditioning on subject
labels during training allows the model to learn a variety of realistic
speaking styles. VOCA also provides animator controls to alter speaking style,
identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball
rotations) during animation. To our knowledge, VOCA is the only realistic 3D
facial animation model that is readily applicable to unseen subjects without
retargeting. This makes VOCA suitable for tasks like in-game video, virtual
reality avatars, or any scenario in which the speaker, speech, or language is
not known in advance. We make the dataset and model available for research
purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201
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