10,225 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
A Survey of Monte Carlo Tree Search Methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems
- …