24,895 research outputs found
A vision-guided parallel parking system for a mobile robot using approximate policy iteration
Reinforcement Learning (RL) methods enable autonomous robots to learn skills from scratch by interacting with the environment. However, reinforcement learning can be very time consuming. This paper focuses on accelerating the reinforcement learning process on a mobile robot in an unknown environment. The presented algorithm is based on approximate policy iteration with a continuous state space and a fixed number of actions. The action-value function is represented by a weighted combination of basis functions.
Furthermore, a complexity analysis is provided to show that the implemented approach is guaranteed to converge on an optimal policy with less computational time.
A parallel parking task is selected for testing purposes. In the experiments, the efficiency of the proposed approach is demonstrated and analyzed through a set of simulated and real robot experiments, with comparison drawn from two well known algorithms (Dyna-Q and Q-learning)
Evaluating the Impact of SDC on the GMRES Iterative Solver
Increasing parallelism and transistor density, along with increasingly
tighter energy and peak power constraints, may force exposure of occasionally
incorrect computation or storage to application codes. Silent data corruption
(SDC) will likely be infrequent, yet one SDC suffices to make numerical
algorithms like iterative linear solvers cease progress towards the correct
answer. Thus, we focus on resilience of the iterative linear solver GMRES to a
single transient SDC. We derive inexpensive checks to detect the effects of an
SDC in GMRES that work for a more general SDC model than presuming a bit flip.
Our experiments show that when GMRES is used as the inner solver of an
inner-outer iteration, it can "run through" SDC of almost any magnitude in the
computationally intensive orthogonalization phase. That is, it gets the right
answer using faulty data without any required roll back. Those SDCs which it
cannot run through, get caught by our detection scheme
Do optimization methods in deep learning applications matter?
With advances in deep learning, exponential data growth and increasing model
complexity, developing efficient optimization methods are attracting much
research attention. Several implementations favor the use of Conjugate Gradient
(CG) and Stochastic Gradient Descent (SGD) as being practical and elegant
solutions to achieve quick convergence, however, these optimization processes
also present many limitations in learning across deep learning applications.
Recent research is exploring higher-order optimization functions as better
approaches, but these present very complex computational challenges for
practical use. Comparing first and higher-order optimization functions, in this
paper, our experiments reveal that Levemberg-Marquardt (LM) significantly
supersedes optimal convergence but suffers from very large processing time
increasing the training complexity of both, classification and reinforcement
learning problems. Our experiments compare off-the-shelf optimization
functions(CG, SGD, LM and L-BFGS) in standard CIFAR, MNIST, CartPole and
FlappyBird experiments.The paper presents arguments on which optimization
functions to use and further, which functions would benefit from
parallelization efforts to improve pretraining time and learning rate
convergence
An Asynchronous Parallel Randomized Kaczmarz Algorithm
We describe an asynchronous parallel variant of the randomized Kaczmarz (RK)
algorithm for solving the linear system . The analysis shows linear
convergence and indicates that nearly linear speedup can be expected if the
number of processors is bounded by a multiple of the number of rows in
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