25,394 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
Model Learning for Look-ahead Exploration in Continuous Control
We propose an exploration method that incorporates look-ahead search over
basic learnt skills and their dynamics, and use it for reinforcement learning
(RL) of manipulation policies . Our skills are multi-goal policies learned in
isolation in simpler environments using existing multigoal RL formulations,
analogous to options or macroactions. Coarse skill dynamics, i.e., the state
transition caused by a (complete) skill execution, are learnt and are unrolled
forward during lookahead search. Policy search benefits from temporal
abstraction during exploration, though itself operates over low-level primitive
actions, and thus the resulting policies does not suffer from suboptimality and
inflexibility caused by coarse skill chaining. We show that the proposed
exploration strategy results in effective learning of complex manipulation
policies faster than current state-of-the-art RL methods, and converges to
better policies than methods that use options or parametrized skills as
building blocks of the policy itself, as opposed to guiding exploration. We
show that the proposed exploration strategy results in effective learning of
complex manipulation policies faster than current state-of-the-art RL methods,
and converges to better policies than methods that use options or parameterized
skills as building blocks of the policy itself, as opposed to guiding
exploration.Comment: This is a pre-print of our paper which is accepted in AAAI 201
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Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
We present an approach for mobile robots to learn to navigate in dynamic
environments with pedestrians via raw depth inputs, in a socially compliant
manner. To achieve this, we adopt a generative adversarial imitation learning
(GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our
approach overcomes the disadvantages of previous methods, as they heavily
depend on the full knowledge of the location and velocity information of nearby
pedestrians, which not only requires specific sensors, but also the extraction
of such state information from raw sensory input could consume much computation
time. In this paper, our proposed GAIL-based model performs directly on raw
depth inputs and plans in real-time. Experiments show that our GAIL-based
approach greatly improves the safety and efficiency of the behavior of mobile
robots from pure behavior cloning. The real-world deployment also shows that
our method is capable of guiding autonomous vehicles to navigate in a socially
compliant manner directly through raw depth inputs. In addition, we release a
simulation plugin for modeling pedestrian behaviors based on the social force
model.Comment: ICRA 2018 camera-ready version. 7 pages, video link:
https://www.youtube.com/watch?v=0hw0GD3lkA
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