18,276 research outputs found
Efficient Object Manipulation Planning with Monte Carlo Tree Search
This paper presents an efficient approach to object manipulation planning
using Monte Carlo Tree Search (MCTS) to find contact sequences and an efficient
ADMM-based trajectory optimization algorithm to evaluate the dynamic
feasibility of candidate contact sequences. To accelerate MCTS, we propose a
methodology to learn a goal-conditioned policy-value network to direct the
search towards promising nodes. Further, manipulation-specific heuristics
enable to drastically reduce the search space. Systematic object manipulation
experiments in a physics simulator and on real hardware demonstrate the
efficiency of our approach. In particular, our approach scales favorably for
long manipulation sequences thanks to the learned policy-value network,
significantly improving planning success rate
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems
In this article, a novel approach to solve combinatorial optimization
problems is proposed. This approach makes use of a heuristic algorithm to
explore the search space tree of a problem instance. The algorithm is based on
Monte Carlo tree search, a popular algorithm in game playing that is used to
explore game trees. By leveraging the combinatorial structure of a problem,
several enhancements to the algorithm are proposed. These enhancements aim to
efficiently explore the search space tree by pruning subtrees, using a
heuristic simulation policy, reducing the domains of variables by eliminating
dominated value assignments and using a beam width. They are demonstrated for
two specific combinatorial optimization problems: the quay crane scheduling
problem with non-crossing constraints and the 0-1 knapsack problem.
Computational results show that the algorithm achieves promising results for
both problems and eight new best solutions for a benchmark set of instances are
found for the former problem. These results indicate that the algorithm is
competitive with the state-of-the-art. Apart from this, the results also show
evidence that the algorithm is able to learn to correct the incorrect choices
made by constructive heuristics
Finding Competitive Network Architectures Within a Day Using UCT
The design of neural network architectures for a new data set is a laborious
task which requires human deep learning expertise. In order to make deep
learning available for a broader audience, automated methods for finding a
neural network architecture are vital. Recently proposed methods can already
achieve human expert level performances. However, these methods have run times
of months or even years of GPU computing time, ignoring hardware constraints as
faced by many researchers and companies. We propose the use of Monte Carlo
planning in combination with two different UCT (upper confidence bound applied
to trees) derivations to search for network architectures. We adapt the UCT
algorithm to the needs of network architecture search by proposing two ways of
sharing information between different branches of the search tree. In an
empirical study we are able to demonstrate that this method is able to find
competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day.
Extending the search time to five GPU days, we are able to outperform human
architectures and our competitors which consider the same types of layers
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