6,785 research outputs found
Closing the loop between neural network simulators and the OpenAI Gym
Since the enormous breakthroughs in machine learning over the last decade,
functional neural network models are of growing interest for many researchers
in the field of computational neuroscience. One major branch of research is
concerned with biologically plausible implementations of reinforcement
learning, with a variety of different models developed over the recent years.
However, most studies in this area are conducted with custom simulation scripts
and manually implemented tasks. This makes it hard for other researchers to
reproduce and build upon previous work and nearly impossible to compare the
performance of different learning architectures. In this work, we present a
novel approach to solve this problem, connecting benchmark tools from the field
of machine learning and state-of-the-art neural network simulators from
computational neuroscience. This toolchain enables researchers in both fields
to make use of well-tested high-performance simulation software supporting
biologically plausible neuron, synapse and network models and allows them to
evaluate and compare their approach on the basis of standardized environments
of varying complexity. We demonstrate the functionality of the toolchain by
implementing a neuronal actor-critic architecture for reinforcement learning in
the NEST simulator and successfully training it on two different environments
from the OpenAI Gym
Exploiting Cognitive Structure for Adaptive Learning
Adaptive learning, also known as adaptive teaching, relies on learning path
recommendation, which sequentially recommends personalized learning items
(e.g., lectures, exercises) to satisfy the unique needs of each learner.
Although it is well known that modeling the cognitive structure including
knowledge level of learners and knowledge structure (e.g., the prerequisite
relations) of learning items is important for learning path recommendation,
existing methods for adaptive learning often separately focus on either
knowledge levels of learners or knowledge structure of learning items. To fully
exploit the multifaceted cognitive structure for learning path recommendation,
we propose a Cognitive Structure Enhanced framework for Adaptive Learning,
named CSEAL. By viewing path recommendation as a Markov Decision Process and
applying an actor-critic algorithm, CSEAL can sequentially identify the right
learning items to different learners. Specifically, we first utilize a
recurrent neural network to trace the evolving knowledge levels of learners at
each learning step. Then, we design a navigation algorithm on the knowledge
structure to ensure the logicality of learning paths, which reduces the search
space in the decision process. Finally, the actor-critic algorithm is used to
determine what to learn next and whose parameters are dynamically updated along
the learning path. Extensive experiments on real-world data demonstrate the
effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19
Training effectiveness assessment: Where are we?
Over 9,000 pilot training courses have been conducted at FSI using the Bell 222 and Sikorsky S-76 simulators. Through the use of FAA exemptions, these simulators can be used for certain training and checking credit. The history of the development and use of commercial helicopter simulators and the opportunities for their increased utilization and use were explored
NASA/FAA helicopter simulator workshop
A workshop was convened by the FAA and NASA for the purpose of providing a forum at which leading designers, manufacturers, and users of helicopter simulators could initiate and participate in a development process that would facilitate the formulation of qualification standards by the regulatory agency. Formal papers were presented, special topics were discussed in breakout sessions, and a draft FAA advisory circular defining specifications for helicopter simulators was presented and discussed. A working group of volunteers was formed to work with the National Simulator Program Office to develop a final version of the circular. The workshop attracted 90 individuals from a constituency of simulator manufacturers, training organizations, the military, civil regulators, research scientists, and five foreign countries
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Simulations are attractive environments for training agents as they provide
an abundant source of data and alleviate certain safety concerns during the
training process. But the behaviours developed by agents in simulation are
often specific to the characteristics of the simulator. Due to modeling error,
strategies that are successful in simulation may not transfer to their real
world counterparts. In this paper, we demonstrate a simple method to bridge
this "reality gap". By randomizing the dynamics of the simulator during
training, we are able to develop policies that are capable of adapting to very
different dynamics, including ones that differ significantly from the dynamics
on which the policies were trained. This adaptivity enables the policies to
generalize to the dynamics of the real world without any training on the
physical system. Our approach is demonstrated on an object pushing task using a
robotic arm. Despite being trained exclusively in simulation, our policies are
able to maintain a similar level of performance when deployed on a real robot,
reliably moving an object to a desired location from random initial
configurations. We explore the impact of various design decisions and show that
the resulting policies are robust to significant calibration error
Pan American World Airways flight training: A new direction. Flight operations resource management
The Pan Am Flight Training Department shares the experiences it is having in its attempt to integrate cockpit resource management philosophies into its training programs. A slide-tape presentation on Pan Am's new direction in flight training is presented and briefly discussed
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer
An important goal of research in Deep Reinforcement Learning in mobile
robotics is to train agents capable of solving complex tasks, which require a
high level of scene understanding and reasoning from an egocentric perspective.
When trained from simulations, optimal environments should satisfy a currently
unobtainable combination of high-fidelity photographic observations, massive
amounts of different environment configurations and fast simulation speeds. In
this paper we argue that research on training agents capable of complex
reasoning can be simplified by decoupling from the requirement of high fidelity
photographic observations. We present a suite of tasks requiring complex
reasoning and exploration in continuous, partially observable 3D environments.
The objective is to provide challenging scenarios and a robust baseline agent
architecture that can be trained on mid-range consumer hardware in under 24h.
Our scenarios combine two key advantages: (i) they are based on a simple but
highly efficient 3D environment (ViZDoom) which allows high speed simulation
(12000fps); (ii) the scenarios provide the user with a range of difficulty
settings, in order to identify the limitations of current state of the art
algorithms and network architectures. We aim to increase accessibility to the
field of Deep-RL by providing baselines for challenging scenarios where new
ideas can be iterated on quickly. We argue that the community should be able to
address challenging problems in reasoning of mobile agents without the need for
a large compute infrastructure
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