Efficient Learning Approach for Dubins Traveling Salesman Problems with Neighborhoods

Khái niệm cốt lõi

A novel learning approach that combines distilling privileged information and reinforcement learning with demonstrations to efficiently solve the Dubins Traveling Salesman Problem with Neighborhoods.

Tóm tắt

The paper presents a novel learning approach called Distilling Privileged information for Dubins Traveling Salesman Problems (DiPDTSP) to efficiently solve the Dubins Traveling Salesman Problem with Neighborhoods (DTSPN).
The key highlights are:

The method involves two learning phases:

In the first phase, a model-free reinforcement learning approach leverages privileged information from expert trajectories generated by the LinKernighan heuristic (LKH) algorithm to distill knowledge.
In the second phase, a supervised learning approach trains an adaptation network to solve DTSPN independently of privileged information.

Before the first learning phase, a parameter initialization technique using demonstration data was devised to enhance training efficiency.

The proposed learning method produces a solution about 50 times faster than LKH and substantially outperforms other imitation learning and reinforcement learning with demonstration schemes, most of which fail to sense all the task points.

Experiments show that the DiPDTSP approach closely follows the expert performance while taking significantly less computation time compared to the heuristic solver.

Thống kê

The proposed DiPDTSP algorithm computes the DTSPN path about 50 times faster than the heuristic method.

Trích dẫn

"The proposed learning method produces a solution about 50 times faster than LKH and substantially outperforms other imitation learning and RL with demonstration schemes, most of which fail to sense all the task points."
"The simulation results show that our work outperforms the existing imitation learning and RL with demonstration methods in sensing all task points. Our method closely follows the expert performance while taking more than 50x less computation time than the heuristic solver."

Thông tin chi tiết chính được chắt lọc từ

Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods

by Min Kyu Shin... lúc arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16721.pdf

Yêu cầu sâu hơn

How can the DiPDTSP approach be extended to handle dynamic environments or partially observable scenarios

The DiPDTSP approach can be extended to handle dynamic environments or partially observable scenarios by incorporating techniques such as recurrent neural networks (RNNs) or Long Short-Term Memory (LSTM) networks. These types of networks can retain information over time, allowing the model to remember past states and actions in dynamic environments. By feeding historical data into the network, it can learn patterns and make more informed decisions in changing scenarios. Additionally, the use of attention mechanisms can help the model focus on relevant information in partially observable environments, improving its decision-making process.

What are the potential limitations or drawbacks of the distillation process, and how can they be addressed

One potential limitation of the distillation process is the risk of overfitting to the expert demonstrations, leading to a lack of generalization to unseen scenarios. To address this, techniques such as regularization methods like dropout or weight decay can be employed to prevent overfitting. Additionally, introducing diversity in the expert demonstrations used for distillation can help the model learn a more robust policy. Another drawback could be the computational complexity of the distillation process, which can be mitigated by optimizing the network architecture and training procedures to be more efficient.

Could the privileged information used in this work be replaced or supplemented by other forms of expert knowledge, such as demonstrations in simulation or human feedback, and how would that affect the performance

The privileged information used in this work could potentially be replaced or supplemented by other forms of expert knowledge, such as demonstrations in simulation or human feedback. Using demonstrations in simulation can provide a larger and more diverse set of expert trajectories for training, leading to a more robust model. Human feedback, on the other hand, can offer nuanced insights that may not be captured in automated demonstrations. By combining different forms of expert knowledge, the model can benefit from a more comprehensive understanding of the task and potentially improve its performance in real-world scenarios.