74,180 research outputs found
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving
Machine learning can provide efficient solutions to the complex problems
encountered in autonomous driving, but ensuring their safety remains a
challenge. A number of authors have attempted to address this issue, but there
are few publicly-available tools to adequately explore the trade-offs between
functionality, scalability, and safety.
We thus present WiseMove, a software framework to investigate safe deep
reinforcement learning in the context of motion planning for autonomous
driving. WiseMove adopts a modular learning architecture that suits our current
research questions and can be adapted to new technologies and new questions. We
present the details of WiseMove, demonstrate its use on a common traffic
scenario, and describe how we use it in our ongoing safe learning research
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
Intrinsically motivated spontaneous exploration is a key enabler of
autonomous lifelong learning in human children. It enables the discovery and
acquisition of large repertoires of skills through self-generation,
self-selection, self-ordering and self-experimentation of learning goals. We
present an algorithmic approach called Intrinsically Motivated Goal Exploration
Processes (IMGEP) to enable similar properties of autonomous or self-supervised
learning in machines. The IMGEP algorithmic architecture relies on several
principles: 1) self-generation of goals, generalized as fitness functions; 2)
selection of goals based on intrinsic rewards; 3) exploration with incremental
goal-parameterized policy search and exploitation of the gathered data with a
batch learning algorithm; 4) systematic reuse of information acquired when
targeting a goal for improving towards other goals. We present a particularly
efficient form of IMGEP, called Modular Population-Based IMGEP, that uses a
population-based policy and an object-centered modularity in goals and
mutations. We provide several implementations of this architecture and
demonstrate their ability to automatically generate a learning curriculum
within several experimental setups including a real humanoid robot that can
explore multiple spaces of goals with several hundred continuous dimensions.
While no particular target goal is provided to the system, this curriculum
allows the discovery of skills that act as stepping stone for learning more
complex skills, e.g. nested tool use. We show that learning diverse spaces of
goals with intrinsic motivations is more efficient for learning complex skills
than only trying to directly learn these complex skills
ATDN vSLAM: An all-through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping
In this paper, a novel solution is introduced for visual Simultaneous
Localization and Mapping (vSLAM) that is built up of Deep Learning components.
The proposed architecture is a highly modular framework in which each component
offers state of the art results in their respective fields of vision-based deep
learning solutions. The paper shows that with the synergic integration of these
individual building blocks, a functioning and efficient all-through deep neural
(ATDN) vSLAM system can be created. The Embedding Distance Loss function is
introduced and using it the ATDN architecture is trained. The resulting system
managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a
subset of the KITTI dataset. The proposed architecture can be used for
efficient and low-latency autonomous driving (AD) aiding database creation as
well as a basis for autonomous vehicle (AV) control.Comment: Published in Periodica Polytechnica Electrical Engineering 11 page
RL4ReAl: Reinforcement Learning for Register Allocation
We propose a novel solution for the Register Allocation problem, leveraging
multi-agent hierarchical Reinforcement Learning. We formalize the constraints
that precisely define the problem for a given instruction-set architecture,
while ensuring that the generated code preserves semantic correctness. We also
develop a gRPC based framework providing a modular and efficient compiler
interface for training and inference. Experimental results match or outperform
the LLVM register allocators, targeting Intel x86 and ARM AArch64
Parallel growing and training of neural networks using output parallelism
In order to find an appropriate architecture for a large-scale real-world application automatically and efficiently, a natural method is to divide the original problem into a set of sub-problems. In this paper, we propose a simple neural network task decomposition method based on output parallelism. By using this method, a problem can be divided flexibly into several sub-problems as chosen, each of which is composed of the whole input vector and a fraction of the output vector. Each module (for one sub-problem) is responsible for producing a fraction of the output vector of the original problem. The hidden structure for the original problem’s output units are decoupled. These modules can be grown and trained in parallel on parallel processing elements. Incorporated with a constructive learning algorithm, our method does not require excessive computation and any prior knowledge concerning decomposition. The feasibility of output parallelism is analyzed and proved. Some benchmarks are implemented to test the validity of this method. Their results show that this method can reduce computational time, increase learning speed and improve generalization accuracy for both classification and regression problems
From Data Topology to a Modular Classifier
This article describes an approach to designing a distributed and modular
neural classifier. This approach introduces a new hierarchical clustering that
enables one to determine reliable regions in the representation space by
exploiting supervised information. A multilayer perceptron is then associated
with each of these detected clusters and charged with recognizing elements of
the associated cluster while rejecting all others. The obtained global
classifier is comprised of a set of cooperating neural networks and completed
by a K-nearest neighbor classifier charged with treating elements rejected by
all the neural networks. Experimental results for the handwritten digit
recognition problem and comparison with neural and statistical nonmodular
classifiers are given
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