In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem (TSP). We adopt a greedy algorithm framework to construct the optimal solution to TSP by adding the nodes successively. A graph neural network (GNN) is trained to capture the local and global graph structure and give the prior probability of selecting each vertex every step. The prior probability provides a heuristics for MCTS, and the MCTS output is an improved probability for selecting the successive vertex, as it is the feedback information by fusing the prior with the scouting procedure. Experimental results on TSP up to 100 nodes demonstrate that the proposed method obtains shorter tours than other learning-based methods.
[A]: A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem


[Q]: We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values. The new Q-estimates are then used in combination with real experience to update the prior. This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search. SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari. SAVE consistently achieves higher rewards with fewer training steps, and---in contrast to typical model-based search approaches---yields strong performance with very small search budgets. By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.
[A]: We propose a model-based method called "Search with Amortized Value Estimates" (SAVE) which leverages both real and planned experience by combining Q-learning with Monte-Carlo Tree Search, achieving strong performance with very small search budgets.


[Q]: In this paper, we propose a novel approach to improve a given surface mapping through local refinement. The approach
 receives an established mapping between two surfaces and follows four phases: (i) inspection of the mapping and creation of a sparse
set of landmarks in mismatching regions; (ii) segmentation with a low-distortion region-growing process based on flattening the
segmented parts; (iii) optimization of the deformation of segmented parts to align the landmarks in the planar parameterization domain;
and (iv) aggregation of the mappings from segments to update the surface mapping. In addition, we propose a new method to deform the
 mesh in order to meet constraints (in our case, the landmark alignment of phase (iii)). We incrementally adjust the cotangent weights for
 the constraints and apply the deformation in a fashion that guarantees that the deformed mesh will be free of flipped faces and will have
 low conformal distortion. Our new deformation approach, Iterative Least Squares Conformal Mapping (ILSCM), outperforms other
 low-distortion deformation methods. The approach is general, and we tested it by improving the mappings from different existing surface
 mapping methods. We also tested its effectiveness by editing the mappings for a variety of 3D objects.
[A]:
We propose a novel approach to improve a given cross-surface mapping through local refinement with a new iterative method to deform the mesh in order to meet user constraints.