Grasper: A Generalist Pursuit-Evasion Algorithm for Diverse Game Scenarios

Conceitos Básicos

Grasper is a novel algorithmic framework that can efficiently solve pursuit-evasion problems across a broad range of scenarios with varying initial conditions, enabling practical deployment in real-world situations.

Resumo

The paper introduces Grasper, a generalist pursuer for pursuit-evasion problems (PEGs). PEGs model the interactions between a team of pursuers and an evader in graph-based environments such as urban street networks.
Key highlights:

Grasper's architecture consists of a graph neural network (GNN) to encode PEGs into hidden vectors, and a hypernetwork to generate pursuer policies based on these hidden vectors. This allows Grasper to efficiently solve diverse PEGs with varying initial conditions.
Grasper employs a three-stage training method:
a. Pre-pretraining stage to train the GNN using self-supervised graph learning techniques like GraphMAE.
b. Pre-training stage that utilizes heuristic-guided multi-task pre-training (HMP) to regularize pursuer policies.
c. Fine-tuning stage that employs PSRO to generate pursuer policies on designated PEGs.
Extensive experiments on synthetic and real-world maps demonstrate Grasper's significant superiority over baselines in terms of solution quality and generalizability.

Estatísticas

"Grasper provides a versatile approach for solving pursuit-evasion problems across a broad range of scenarios, enabling practical deployment in real-world situations."
"Grasper can start from and converge to a higher average worst-case utility than the baselines, although it takes a certain pre-training time, demonstrating the effectiveness of the pre-pretraining and pre-training in accelerating the PSRO procedure."

Citações

"Grasper is a GeneRAlist purSuer for Pursuit-Evasion pRoblems, capable of efficiently generating pursuer policies tailored to specific PEGs."
"To address this issue, we introduce Grasper, a GeneRAlist purSuer for Pursuit-Evasion pRoblems, capable of efficiently generating pursuer policies tailored to specific PEGs."

Principais Insights Extraídos De

Grasper: A Generalist Pursuer for Pursuit-Evasion Problems

by Pengdeng Li,... às arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12626.pdf

Perguntas Mais Profundas

How can the proposed Grasper framework be extended to handle more complex game dynamics, such as partial observability or stochastic environments

The Grasper framework can be extended to handle more complex game dynamics by incorporating techniques to address partial observability and stochastic environments.
For partial observability, one approach could be to integrate recurrent neural networks (RNNs) or long short-term memory (LSTM) networks into the architecture. These networks can capture temporal dependencies in the observations, allowing the pursuer to make decisions based on a history of observations rather than just the current state. By incorporating memory mechanisms, the pursuer can maintain an internal state that retains information about past observations, enabling better decision-making in partially observable environments.
In stochastic environments, where outcomes are not deterministic, Grasper can be enhanced by incorporating probabilistic models. This could involve using probabilistic graphical models or Bayesian neural networks to capture uncertainty in the environment. By modeling the stochasticity explicitly, Grasper can adapt its strategies to account for the inherent randomness in the environment.
Additionally, reinforcement learning algorithms that are specifically designed for handling partial observability, such as partially observable Markov decision processes (POMDPs), could be integrated into the training pipeline of Grasper. These algorithms explicitly model the uncertainty in the environment and the agent's observations, allowing for more robust decision-making in complex and uncertain scenarios.

What are the potential limitations of the heuristic-guided multi-task pre-training (HMP) approach, and how could it be further improved

The heuristic-guided multi-task pre-training (HMP) approach in Grasper has certain limitations that could be addressed for further improvement:

Heuristic Bias: One limitation of HMP is the potential bias introduced by the heuristic reference policy. If the heuristic policy is not well-designed or does not accurately capture the optimal strategies, it may lead to suboptimal learning. To mitigate this limitation, a more sophisticated heuristic policy could be developed using domain knowledge or reinforcement learning techniques to ensure that it provides informative guidance to the learning agent.

Generalization to Diverse Scenarios: HMP may struggle to generalize effectively to diverse scenarios that deviate significantly from the training set. To improve generalization, the HMP approach could be augmented with techniques such as curriculum learning, where the difficulty of the training scenarios is gradually increased, or domain randomization, where the agent is exposed to a wide range of environmental variations during training.

Exploration-Exploitation Trade-off: HMP relies on a balance between exploration guided by the heuristic policy and exploitation of the learned policies. Ensuring that the agent explores sufficiently to discover new strategies while leveraging the heuristic guidance effectively is crucial. Techniques like epsilon-greedy exploration or intrinsic motivation could be incorporated to enhance exploration in the learning process.

By addressing these limitations, the HMP approach in Grasper can be further refined to improve the efficiency and effectiveness of the pre-training process.

Given the generalization capabilities of Grasper, how could it be applied to other types of multi-agent decision-making problems beyond pursuit-evasion games

The generalization capabilities of Grasper can be applied to various other multi-agent decision-making problems beyond pursuit-evasion games. Some potential applications include:

Traffic Management: Grasper could be utilized to optimize traffic flow in urban environments by coordinating the actions of autonomous vehicles, traffic lights, and pedestrians. The framework's ability to adapt to different scenarios and generate tailored policies could enhance traffic efficiency and reduce congestion.

Supply Chain Optimization: In supply chain management, Grasper could assist in optimizing inventory management, distribution routes, and resource allocation. By learning from diverse supply chain configurations and dynamics, Grasper could provide adaptive and efficient decision-making strategies.

Multi-Robot Coordination: Grasper could be applied to coordinate the actions of multiple robots in tasks such as search and rescue missions, warehouse automation, or environmental monitoring. The framework's ability to generate policies based on specific task requirements and environmental conditions could improve the overall coordination and performance of robot teams.

By leveraging Grasper's generalization capabilities and adapting the framework to the specific requirements of different multi-agent decision-making problems, it can offer scalable and efficient solutions in a wide range of real-world applications.