Provably Efficient Information-Directed Sampling Algorithms for Learning Nash Equilibrium in Multi-Agent Reinforcement Learning

Основні поняття

This work introduces novel information-directed sampling (IDS) algorithms that are proven to be sample-efficient for learning Nash equilibrium in multi-agent reinforcement learning settings, including two-player zero-sum Markov games and multi-player general-sum Markov games.

Анотація

This paper presents a set of novel algorithms based on the principle of information-directed sampling (IDS) for multi-agent reinforcement learning (MARL) problems. The key contributions are:

1. MAIDS Algorithm:

   • Designed for two-player zero-sum Markov games (MGs)
   • Employs an asymmetric learning structure where the max-player solves a minimax optimization problem based on the joint information ratio, and the min-player minimizes the marginal information ratio
   • Achieves a Bayesian regret of Õ(√K) for K episodes

2. Reg-MAIDS Algorithm:

   • An improved version of MAIDS with reduced computational complexity while maintaining the same Bayesian regret bound

3. Compressed-MAIDS Algorithm:

   • Leverages the flexibility of IDS in choosing the learning target
   • Constructs a compressed environment based on rate-distortion theory and uses it as the learning target
   • Provides improved regret bounds compared to learning the full environment

4. Extension to Multi-Player General-Sum MGs:

   • The Reg-MAIDS algorithm is extended to multi-player general-sum MGs
   • Can learn either the Nash equilibrium or coarse correlated equilibrium in a sample-efficient manner

The key innovation is the application of the IDS principle to the competitive and cooperative multi-agent setting, which was previously unexplored. The algorithms are proven to achieve favorable sample efficiency, computational efficiency, and flexibility in choosing the learning target.

Статистика

None

Цитати

None

Ключові висновки, отримані з

Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning

by Qiaosheng Zh... о arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19292.pdf

Глибші Запити

What are the potential applications of the proposed IDS-based MARL algorithms in real-world multi-agent systems

The proposed IDS-based MARL algorithms have the potential to be applied in various real-world multi-agent systems where multiple agents need to learn and make sequential decisions in a shared environment. Some potential applications include:

Robot Systems: In scenarios where multiple robots need to collaborate and make decisions to achieve a common goal, IDS-based algorithms can help optimize their decision-making process and improve overall efficiency.

Autonomous Driving: In the context of autonomous vehicles, multiple agents (vehicles) need to interact and make decisions in a dynamic environment. IDS algorithms can help these agents learn optimal strategies for safe and efficient navigation on the roads.

Multi-Player Games: In the gaming industry, where multiple players interact in virtual environments, IDS-based algorithms can enhance the gameplay experience by enabling agents to learn and adapt to the strategies of other players.

Supply Chain Management: In complex supply chain networks where multiple entities need to coordinate their actions, IDS algorithms can assist in optimizing decision-making processes to improve efficiency and reduce costs.

Multi-Agent Communication Systems: In communication networks with multiple agents, IDS algorithms can help in optimizing resource allocation, routing decisions, and overall network performance.

Overall, the proposed IDS-based MARL algorithms can be beneficial in a wide range of real-world applications that involve multiple agents interacting in shared environments.

How can the concept of compressed environments be further extended or generalized to handle more complex information structures in MARL

The concept of compressed environments can be further extended or generalized in MARL to handle more complex information structures in the following ways:

Hierarchical Compression: Instead of compressing the entire environment into a single compressed version, hierarchical compression can be used to create multiple levels of compressed environments. This hierarchical approach can help in handling complex information structures by capturing different levels of abstraction in the environment.

Dynamic Compression: Introducing dynamic compression techniques where the level of compression can adapt based on the complexity of the environment or the learning progress of the agents. This dynamic approach can help in efficiently handling varying levels of information complexity.

Adaptive Distortion Measures: Developing adaptive distortion measures that can capture the specific characteristics of the environment and the learning task. By tailoring the distortion measure to the specific requirements of the task, the compressed environments can better represent the essential information for decision-making.

Multi-Resolution Compression: Implementing multi-resolution compression techniques where different parts of the environment are compressed at varying levels of detail. This approach can help in efficiently representing complex information structures while maintaining the essential details for decision-making.

By exploring these extensions and generalizations, the concept of compressed environments in MARL can be further enhanced to handle more intricate information structures effectively.

What are the potential connections between the information-theoretic quantities used in the IDS principle and other complexity measures like the decision-estimation coefficient (DEC) in the context of multi-agent decision making

The potential connections between the information-theoretic quantities used in the IDS principle and other complexity measures like the decision-estimation coefficient (DEC) in the context of multi-agent decision making include:

Information Acquisition and Decision Complexity: The information-theoretic quantities in the IDS principle, such as mutual information and information ratio, can be related to the decision complexity measured by DEC. Higher mutual information or information ratio may indicate a higher level of information acquisition, which can impact the decision complexity for agents in multi-agent systems.

Trade-off between Information and Decision Accuracy: There may exist a trade-off between acquiring more information about the environment (as measured by mutual information) and making accurate decisions (as measured by DEC). Agents may need to balance the amount of information acquired with the decision complexity to achieve optimal performance in multi-agent decision-making tasks.

Optimization of Decision-Estimation Coefficients: The IDS principle can potentially be used to optimize decision-estimation coefficients in multi-agent systems. By leveraging information-directed sampling techniques, agents can make informed decisions that minimize estimation errors and improve decision accuracy, leading to more efficient decision-making processes.

Joint Analysis of Complexity Measures: Integrating information-theoretic quantities and complexity measures like DEC in a joint analysis can provide a comprehensive understanding of the decision-making process in multi-agent systems. By considering both information acquisition and decision complexity, agents can optimize their strategies for improved performance.

Overall, exploring the connections between information-theoretic quantities and complexity measures like DEC can offer valuable insights into the decision-making dynamics of multi-agent systems.