Improving Scalability and Success Rates of Learnt Local Multi-Agent Path Finding Policies using Heuristic Search

Conceptos Básicos

Learnt local MAPF policies can be significantly improved in terms of success rate and scalability by using them in conjunction with heuristic search techniques like PIBT and LaCAM.

Resumen

The content discusses methods to improve the performance of learnt local multi-agent path finding (MAPF) policies by combining them with heuristic search techniques.
The key insights are:

Naive collision shielding used in existing learnt MAPF policies can cause deadlock. Using a PIBT-based collision shield (CS-PIBT) that considers the full action probability distribution can significantly improve success rates and scalability.

Integrating a learnt local policy with the LaCAM framework, which performs a full horizon search over joint agent configurations, further boosts performance by enabling backtracking and escaping local minima.

Different ways of combining the learnt policy with a heuristic (e.g. backward Dijkstra's) are explored. Tie-breaking actions using the learnt policy preferences is found to improve solution costs compared to using just the heuristic or the learnt policy alone.

The authors provide a nuanced view on when learnt MAPF policies may be beneficial compared to classical heuristic search methods. They argue that learnt policies can outperform heuristic search when the heuristic is imperfect, such as in partially observable or high-dimensional MAPF scenarios where computing a strong heuristic is challenging.

Overall, the content demonstrates how heuristic search can be effectively leveraged to significantly boost the performance of learnt local MAPF policies.

Estadísticas

The content does not contain any explicit numerical data or metrics. The key insights are qualitative in nature, focusing on improving the success rate, scalability, and solution cost of learnt MAPF policies by combining them with heuristic search techniques.

Citas

The content does not contain any direct quotes that are crucial to the key insights.

Ideas clave extraídas de

Improving Learnt Local MAPF Policies with Heuristic Search

by Rishi Veerap... a las arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20300.pdf

Consultas más profundas

How can the proposed techniques be extended to other MAPF variants beyond the single-shot 2D scenario, such as lifelong MAPF or partially observable MAPF

The proposed techniques can be extended to other MAPF variants beyond the single-shot 2D scenario by adapting them to suit the specific requirements of each variant. For lifelong MAPF, where agents immediately move to a different goal upon reaching their first goal, the techniques can be modified to handle continuous movement and goal changes. The collision shielding method, such as CS-PIBT, can be adjusted to accommodate dynamic goal assignments and continuous path planning. Additionally, incorporating a learned policy with LaCAM can enable agents to plan for multiple goal changes over time, ensuring efficient and collision-free movement in lifelong MAPF scenarios.
For partially observable MAPF, where agents have limited visibility of the environment, the techniques can be enhanced to account for uncertainty and incomplete information. The learned policies can be trained to make decisions based on partial observations, taking into consideration the agent's current knowledge of the environment. Integrating heuristic search methods like PIBT and LaCAM with learned policies can help agents navigate through partially observable environments by leveraging both learned behaviors and efficient search algorithms to make informed decisions.
In essence, by customizing the collision shielding, full horizon planning, and combination of learned policies with heuristic search techniques to suit the specific characteristics of lifelong and partially observable MAPF variants, the proposed techniques can be extended to address a broader range of multi-agent pathfinding scenarios.

What are the computational trade-offs between the different ways of combining a learnt policy with a heuristic (e.g. Otie vs Osum(R))

The computational trade-offs between the different ways of combining a learned policy with a heuristic, such as Otie vs Osum(R), lie in the balance between leveraging the learned policy's preferences and the heuristic's guidance. Otie, which prioritizes actions based on the learned policy and breaks ties using the heuristic, tends to perform well in scenarios where the learned policy provides valuable insights into agent behaviors. On the other hand, Osum(R), which combines the heuristic with the learned policy using a weighted parameter R, offers flexibility in adjusting the influence of the learned policy on decision-making.
To optimize these trade-offs for specific MAPF applications, one can consider the following strategies:

Performance Evaluation: Conduct thorough performance evaluations using different combinations of learned policies and heuristics on a diverse set of MAPF scenarios. Analyze the success rates, solution costs, and computational efficiency of each approach to identify the most effective combination for the specific application.
Hyperparameter Tuning: Fine-tune the parameters, such as R in Osum(R), to find the optimal balance between the learned policy and the heuristic. Experiment with different values of the parameter to determine the impact on solution quality and computational complexity.
Scenario-Specific Optimization: Tailor the combination of learned policies and heuristics based on the characteristics of the MAPF scenario. For instance, in scenarios with high agent density or complex obstacle configurations, a more heuristic-driven approach may be beneficial, while in scenarios with dynamic goals, a learned policy-centric approach could be more effective.

By carefully analyzing the computational trade-offs and optimizing the combination of learned policies with heuristic search techniques based on the specific requirements of the MAPF application, one can achieve the best balance between solution quality and computational efficiency.

How can these trade-offs be optimized for specific MAPF applications

While PIBT and LaCAM are effective heuristic search techniques for integrating with learned MAPF policies, there are other methods that could also enhance performance when combined with learned policies. Some additional heuristic search techniques that could be effectively integrated include:

Conflict-Based Search (CBS): CBS is a popular heuristic search algorithm for MAPF that decomposes the problem into individual agent paths and resolves conflicts iteratively. By combining CBS with learned policies, agents can benefit from both the efficiency of CBS in resolving conflicts and the learned behaviors for decision-making.

A Search*: A* search is a widely used algorithm in pathfinding that combines the advantages of breadth-first and greedy best-first search. By integrating A* search with learned policies, agents can leverage the heuristic information provided by A* to guide their actions while incorporating learned behaviors for adaptive decision-making.

D Lite*: D* Lite is an incremental heuristic search algorithm designed for dynamic environments where the cost of actions can change. By combining D* Lite with learned policies, agents can adapt to changing environments while utilizing the efficiency of D* Lite in updating paths incrementally.

Potential Fields: Potential fields is a method that generates artificial forces to guide agents towards their goals while avoiding obstacles. Integrating potential fields with learned policies can provide a smooth and efficient path planning strategy that combines the benefits of both approaches.

By exploring the integration of these and other heuristic search techniques with learned MAPF policies, researchers can further enhance the performance and scalability of multi-agent pathfinding systems in diverse scenarios.