Variational Offline Multi-agent Skill Discovery

Jiayu Chen, Bhargav Ganguly, Tian Lan, Vaneet Aggarwal·May 26, 2024

Summary

This paper presents two novel auto-encoder models, VO-MASD-3D and VO-MASD-Hier, for skill discovery in multi-agent scenarios. These methods address the challenge of extracting subgroup coordination patterns in offline multi-agent tasks, focusing on capturing both subgroup and temporal abstractions. By using a dynamic grouping function, the models detect latent subgroups and enable efficient learning and coordination in MARL tasks with delayed or sparse rewards. The key contributions include: 1. **Skill extraction**: The methods learn multi-agent skills from offline data, forming flexible representations that can be transferred across tasks without retraining. 2. **Dynamic grouping**: The dynamic grouping function identifies agent interactions to form subgroups, improving task decomposition and collaboration. 3. **Performance**: VO-MASD-3D and VO-MASD-Hier outperform existing approaches in applying skills in MARL, especially in long-horizon StarCraft tasks and sparse reward environments. 4. **Hierarchical design**: VO-MASD-Hier uses a hierarchical codebook for better abstraction, facilitating skill representation and generalization. The research evaluates the models' effectiveness in online MARL tasks, showing superior performance compared to state-of-the-art methods, particularly in scenarios with sparse rewards. The study highlights the potential of multi-agent skills for simplifying the MARL process and improving collaboration in complex tasks. However, it also suggests areas for future improvement, such as adapting the method for heterogeneous agents and optimizing skill assignment for large teams.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of automatically extracting subgroup coordination patterns in a multi-agent task by proposing two novel auto-encoder schemes: VO-MASD-3D and VO-MASD-Hier, which capture subgroup- and temporal-level abstractions to form multi-agent skills . This problem is not entirely new, as there have been research gaps in multi-agent scenarios regarding the extraction of coordination patterns among subgroups in a multi-agent task . The proposed schemes with a dynamic grouping function can automatically detect latent subgroups based on agent interactions, enabling the transfer of discovered subgroup skills across relevant tasks without the need for retraining .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that the proposed auto-encoder schemes, VO-MASD-3D and VO-MASD-Hier, can effectively capture subgroup- and temporal-level abstractions to form multi-agent skills in order to automatically extract subgroup coordination patterns in a multi-agent task . The essential algorithm component of these schemes involves a dynamic grouping function that can detect latent subgroups based on agent interactions in a task . The study focuses on applying these methods to offline multi-task data and transferring the discovered subgroup skills across relevant tasks without the need for retraining .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Variational Offline Multi-agent Skill Discovery" proposes innovative ideas, methods, and models for skill discovery in multi-agent reinforcement learning scenarios . The key contributions of the paper include:

  • Novel Auto-Encoder Schemes: The paper introduces two novel auto-encoder schemes, namely VO-MASD-3D and VO-MASD-Hier, designed to capture subgroup- and temporal-level abstractions simultaneously to form multi-agent skills .

  • Dynamic Grouping Function: An essential component of the proposed schemes is a dynamic grouping function that automatically detects latent subgroups based on agent interactions in a task, facilitating the extraction of subgroup coordination patterns in multi-agent tasks .

  • Transferability of Discovered Skills: The method developed in the paper can be applied to offline multi-task data, enabling the transfer of discovered subgroup skills across relevant tasks without the need for retraining, thus enhancing efficiency and performance in multi-agent reinforcement learning scenarios .

  • Empirical Evaluations: Empirical evaluations conducted on StarCraft tasks demonstrate that the approach significantly outperforms existing methods in applying skills in multi-agent reinforcement learning. The discovered skills using the proposed method effectively reduce the learning difficulty in MARL scenarios with delayed and sparse reward signals . The paper "Variational Offline Multi-agent Skill Discovery" introduces innovative characteristics and advantages compared to previous methods in skill discovery for multi-agent reinforcement learning scenarios . Here are the key points based on the details in the paper:

  • Novel Auto-Encoder Schemes: The paper proposes two novel auto-encoder schemes, VO-MASD-3D and VO-MASD-Hier, which capture subgroup- and temporal-level abstractions simultaneously to form multi-agent skills . These schemes address the challenge of automatically extracting subgroup coordination patterns in multi-agent tasks, enhancing the efficiency of hierarchical learning for long-horizon tasks .

  • Dynamic Grouping Function: An essential component of the proposed schemes is a dynamic grouping function that automatically detects latent subgroups based on agent interactions in a task . This function facilitates the extraction of subgroup coordination patterns, enabling the discovery of multi-agent skills that can be transferred across relevant tasks without the need for retraining .

  • Transferability and Performance: The method developed in the paper allows for the transfer of discovered subgroup skills across tasks without retraining, leading to improved performance in multi-agent reinforcement learning scenarios . Empirical evaluations on StarCraft tasks demonstrate that the approach significantly outperforms existing methods in applying skills in multi-agent reinforcement learning, particularly in scenarios with delayed and sparse reward signals .

  • Hierarchical Learning and Skill Utilization: The paper highlights the advantage of hierarchical learning with discovered skills, where only a high-level policy for skill selection is required for downstream task learning . This hierarchical learning approach is particularly advantageous for long-horizon tasks with sparse and delayed reward signals, as it reduces the decision horizon of the policy, enhancing performance in challenging tasks .

  • Superiority in Challenging Settings: In challenging task settings like MMM2, VO-MASD-Hier demonstrates better results compared to other algorithms, showcasing the potential benefits of utilizing discovered multi-agent skills as complete units . The evaluation on MMM2, known as a super-hard task setting, highlights the superiority of VO-MASD-Hier over other algorithms, emphasizing its effectiveness in challenging scenarios .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of multi-agent skill discovery. Noteworthy researchers in this area include Jiayu Chen, Bhargav Ganguly, Tian Lan, and Vaneet Aggarwal . They have proposed novel auto-encoder schemes, such as VO-MASD-3D and VO-MASD-Hier, to capture subgroup- and temporal-level abstractions for forming multi-agent skills . The key to the solution mentioned in the paper involves developing encoder-decoder architectures and co-training them with a grouping function that dynamically groups agents to extract multi-agent skills effectively .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the discovered multi-agent skills in online Multi-Agent Reinforcement Learning (MARL) . The experiments were conducted on the StarCraft multi-agent challenge (SMAC), a benchmark for cooperative MARL, using extended task sets such as 'marine' and 'MMMs' . The skills were first discovered from offline trajectories of source tasks and then applied to each task in the task set, including both source and unseen tasks, for online MARL . The evaluation compared the skills discovered using different algorithms on the task sets to demonstrate the superiority of the multi-agent skills discovered by their methods . Additionally, the experiments aimed to show that hierarchical learning with skills discovered using their methods can significantly outperform traditional MARL algorithms, especially in tasks with sparse reward signals .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is DH, which is a multi-task offline dataset with trajectories segmented every H time steps . The code for the research project is open source and can be accessed in the released code folder, where the provided hyperparameter configurations are recommended for use .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The research conducted extensive experiments on multi-agent reinforcement learning (MARL) tasks, specifically focusing on skill discovery and its application in downstream MARL tasks . The experiments were carried out on the StarCraft multi-agent challenge (SMAC), a well-established benchmark for cooperative MARL, using extended task sets to evaluate the discovered multi-task multi-agent skills .

The paper demonstrated the effectiveness of the discovered multi-agent skills in online MARL tasks, showcasing superior performance compared to traditional MARL algorithms, especially in scenarios with sparse reward signals . By applying the skills discovered from offline trajectories to various tasks within the task sets, including source and unseen tasks, the research highlighted the practical utility of the discovered skills in enhancing MARL performance .

Furthermore, the experiments conducted in the paper involved comparing skills discovered using different algorithms on the task sets, emphasizing the superiority of the multi-agent skills discovered through their methods . The results indicated that hierarchical learning with the discovered skills significantly outperformed standard MARL algorithms, particularly in tasks with sparse reward signals .

Overall, the experiments and results presented in the paper provide strong empirical evidence supporting the scientific hypotheses related to multi-agent skill discovery and its application in improving performance in downstream MARL tasks, validating the effectiveness of the proposed methods in enhancing cooperative multi-agent reinforcement learning .


What are the contributions of this paper?

The paper "Variational Offline Multi-agent Skill Discovery" makes several key contributions:

  • Novel Auto-encoder Schemes: The paper proposes two innovative auto-encoder schemes, VO-MASD-3D and VO-MASD-Hier, to capture subgroup- and temporal-level abstractions simultaneously, enabling the formation of multi-agent skills .
  • Dynamic Grouping Function: An essential component of the proposed schemes is a dynamic grouping function that automatically detects latent subgroups based on agent interactions in a task, facilitating the extraction of coordination patterns in multi-agent scenarios .
  • Transferability of Discovered Skills: The method developed in the paper can be applied to offline multi-task data, allowing the discovered subgroup skills to be transferred across relevant tasks without the need for retraining, enhancing efficiency and performance in multi-agent reinforcement learning tasks .
  • Empirical Evaluations: Empirical evaluations conducted on StarCraft tasks demonstrate that the approach significantly outperforms existing methods in applying skills in multi-agent reinforcement learning. The discovered skills also prove effective in reducing learning difficulty in scenarios with delayed and sparse reward signals .

What work can be continued in depth?

To further advance the research in multi-agent reinforcement learning (MARL), a potential area for future exploration is the development of a co-training scheme involving the high-level policy and grouper for online MARL. This scheme aims to fine-tune the grouping function with task-specific rewards to enhance performance . Additionally, exploring the integration of discovered subgroup skills into the paradigm of centralized training with decentralized execution (CTDE) could be beneficial. This approach could leverage the stability of centralized training and the applicability of decentralized execution to improve the overall effectiveness of MARL algorithms .


Introduction
Background
Evolution of multi-agent reinforcement learning (MARL)
Challenges in extracting coordination patterns in offline tasks
Objective
To develop novel auto-encoder models for skill discovery in MARL
Addressing subgroup and temporal abstraction in multi-agent scenarios
Methodology
Model Architecture
1. VO-MASD-3D
3D auto-encoder design
Dynamic grouping function for subgroup detection
2. VO-MASD-Hier
Hierarchical design with a codebook
Temporal abstraction for improved skill representation
Skill Extraction
Learning from offline data
Transferability across tasks
Data Collection
Offline multi-agent task datasets
Importance of delayed or sparse rewards
Data Preprocessing
Preprocessing techniques for efficient learning
Handling agent interactions and observations
Dynamic Grouping
Function for identifying and adapting subgroups
Task decomposition and collaboration enhancement
Performance Evaluation
1. Experimental Setup
StarCraft tasks and sparse reward environments
2. Comparison with State-of-the-Art
Performance metrics and results
3. Hierarchical Advantage
Improved performance in long-horizon tasks
Results and Discussion
Online MARL Performance
Superior performance in complex scenarios
Comparison with existing methods
Limitations and Future Directions
Heterogeneous agents adaptation
Skill assignment optimization for large teams
Conclusion
The significance of multi-agent skills in simplifying MARL
Potential for enhancing collaboration in complex tasks
Directions for future research and improvements
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How do these models perform compared to existing approaches, particularly in long-horizon StarCraft tasks and environments with sparse rewards?
How do these models address the challenge of extracting subgroup coordination patterns in offline multi-agent tasks?
What are the key contributions of VO-MASD-3D and VO-MASD-Hier in terms of skill extraction and task decomposition?
What are the two novel auto-encoder models presented in the paper for skill discovery in multi-agent scenarios?

Variational Offline Multi-agent Skill Discovery

Jiayu Chen, Bhargav Ganguly, Tian Lan, Vaneet Aggarwal·May 26, 2024

Summary

This paper presents two novel auto-encoder models, VO-MASD-3D and VO-MASD-Hier, for skill discovery in multi-agent scenarios. These methods address the challenge of extracting subgroup coordination patterns in offline multi-agent tasks, focusing on capturing both subgroup and temporal abstractions. By using a dynamic grouping function, the models detect latent subgroups and enable efficient learning and coordination in MARL tasks with delayed or sparse rewards. The key contributions include: 1. **Skill extraction**: The methods learn multi-agent skills from offline data, forming flexible representations that can be transferred across tasks without retraining. 2. **Dynamic grouping**: The dynamic grouping function identifies agent interactions to form subgroups, improving task decomposition and collaboration. 3. **Performance**: VO-MASD-3D and VO-MASD-Hier outperform existing approaches in applying skills in MARL, especially in long-horizon StarCraft tasks and sparse reward environments. 4. **Hierarchical design**: VO-MASD-Hier uses a hierarchical codebook for better abstraction, facilitating skill representation and generalization. The research evaluates the models' effectiveness in online MARL tasks, showing superior performance compared to state-of-the-art methods, particularly in scenarios with sparse rewards. The study highlights the potential of multi-agent skills for simplifying the MARL process and improving collaboration in complex tasks. However, it also suggests areas for future improvement, such as adapting the method for heterogeneous agents and optimizing skill assignment for large teams.
Mind map
Improved performance in long-horizon tasks
Performance metrics and results
StarCraft tasks and sparse reward environments
Temporal abstraction for improved skill representation
Hierarchical design with a codebook
Dynamic grouping function for subgroup detection
3D auto-encoder design
Skill assignment optimization for large teams
Heterogeneous agents adaptation
Comparison with existing methods
Superior performance in complex scenarios
3. Hierarchical Advantage
2. Comparison with State-of-the-Art
1. Experimental Setup
Task decomposition and collaboration enhancement
Function for identifying and adapting subgroups
Handling agent interactions and observations
Preprocessing techniques for efficient learning
Importance of delayed or sparse rewards
Offline multi-agent task datasets
Transferability across tasks
Learning from offline data
2. VO-MASD-Hier
1. VO-MASD-3D
Addressing subgroup and temporal abstraction in multi-agent scenarios
To develop novel auto-encoder models for skill discovery in MARL
Challenges in extracting coordination patterns in offline tasks
Evolution of multi-agent reinforcement learning (MARL)
Directions for future research and improvements
Potential for enhancing collaboration in complex tasks
The significance of multi-agent skills in simplifying MARL
Limitations and Future Directions
Online MARL Performance
Performance Evaluation
Dynamic Grouping
Data Preprocessing
Data Collection
Skill Extraction
Model Architecture
Objective
Background
Conclusion
Results and Discussion
Methodology
Introduction
Outline
Introduction
Background
Evolution of multi-agent reinforcement learning (MARL)
Challenges in extracting coordination patterns in offline tasks
Objective
To develop novel auto-encoder models for skill discovery in MARL
Addressing subgroup and temporal abstraction in multi-agent scenarios
Methodology
Model Architecture
1. VO-MASD-3D
3D auto-encoder design
Dynamic grouping function for subgroup detection
2. VO-MASD-Hier
Hierarchical design with a codebook
Temporal abstraction for improved skill representation
Skill Extraction
Learning from offline data
Transferability across tasks
Data Collection
Offline multi-agent task datasets
Importance of delayed or sparse rewards
Data Preprocessing
Preprocessing techniques for efficient learning
Handling agent interactions and observations
Dynamic Grouping
Function for identifying and adapting subgroups
Task decomposition and collaboration enhancement
Performance Evaluation
1. Experimental Setup
StarCraft tasks and sparse reward environments
2. Comparison with State-of-the-Art
Performance metrics and results
3. Hierarchical Advantage
Improved performance in long-horizon tasks
Results and Discussion
Online MARL Performance
Superior performance in complex scenarios
Comparison with existing methods
Limitations and Future Directions
Heterogeneous agents adaptation
Skill assignment optimization for large teams
Conclusion
The significance of multi-agent skills in simplifying MARL
Potential for enhancing collaboration in complex tasks
Directions for future research and improvements

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of automatically extracting subgroup coordination patterns in a multi-agent task by proposing two novel auto-encoder schemes: VO-MASD-3D and VO-MASD-Hier, which capture subgroup- and temporal-level abstractions to form multi-agent skills . This problem is not entirely new, as there have been research gaps in multi-agent scenarios regarding the extraction of coordination patterns among subgroups in a multi-agent task . The proposed schemes with a dynamic grouping function can automatically detect latent subgroups based on agent interactions, enabling the transfer of discovered subgroup skills across relevant tasks without the need for retraining .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that the proposed auto-encoder schemes, VO-MASD-3D and VO-MASD-Hier, can effectively capture subgroup- and temporal-level abstractions to form multi-agent skills in order to automatically extract subgroup coordination patterns in a multi-agent task . The essential algorithm component of these schemes involves a dynamic grouping function that can detect latent subgroups based on agent interactions in a task . The study focuses on applying these methods to offline multi-task data and transferring the discovered subgroup skills across relevant tasks without the need for retraining .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Variational Offline Multi-agent Skill Discovery" proposes innovative ideas, methods, and models for skill discovery in multi-agent reinforcement learning scenarios . The key contributions of the paper include:

  • Novel Auto-Encoder Schemes: The paper introduces two novel auto-encoder schemes, namely VO-MASD-3D and VO-MASD-Hier, designed to capture subgroup- and temporal-level abstractions simultaneously to form multi-agent skills .

  • Dynamic Grouping Function: An essential component of the proposed schemes is a dynamic grouping function that automatically detects latent subgroups based on agent interactions in a task, facilitating the extraction of subgroup coordination patterns in multi-agent tasks .

  • Transferability of Discovered Skills: The method developed in the paper can be applied to offline multi-task data, enabling the transfer of discovered subgroup skills across relevant tasks without the need for retraining, thus enhancing efficiency and performance in multi-agent reinforcement learning scenarios .

  • Empirical Evaluations: Empirical evaluations conducted on StarCraft tasks demonstrate that the approach significantly outperforms existing methods in applying skills in multi-agent reinforcement learning. The discovered skills using the proposed method effectively reduce the learning difficulty in MARL scenarios with delayed and sparse reward signals . The paper "Variational Offline Multi-agent Skill Discovery" introduces innovative characteristics and advantages compared to previous methods in skill discovery for multi-agent reinforcement learning scenarios . Here are the key points based on the details in the paper:

  • Novel Auto-Encoder Schemes: The paper proposes two novel auto-encoder schemes, VO-MASD-3D and VO-MASD-Hier, which capture subgroup- and temporal-level abstractions simultaneously to form multi-agent skills . These schemes address the challenge of automatically extracting subgroup coordination patterns in multi-agent tasks, enhancing the efficiency of hierarchical learning for long-horizon tasks .

  • Dynamic Grouping Function: An essential component of the proposed schemes is a dynamic grouping function that automatically detects latent subgroups based on agent interactions in a task . This function facilitates the extraction of subgroup coordination patterns, enabling the discovery of multi-agent skills that can be transferred across relevant tasks without the need for retraining .

  • Transferability and Performance: The method developed in the paper allows for the transfer of discovered subgroup skills across tasks without retraining, leading to improved performance in multi-agent reinforcement learning scenarios . Empirical evaluations on StarCraft tasks demonstrate that the approach significantly outperforms existing methods in applying skills in multi-agent reinforcement learning, particularly in scenarios with delayed and sparse reward signals .

  • Hierarchical Learning and Skill Utilization: The paper highlights the advantage of hierarchical learning with discovered skills, where only a high-level policy for skill selection is required for downstream task learning . This hierarchical learning approach is particularly advantageous for long-horizon tasks with sparse and delayed reward signals, as it reduces the decision horizon of the policy, enhancing performance in challenging tasks .

  • Superiority in Challenging Settings: In challenging task settings like MMM2, VO-MASD-Hier demonstrates better results compared to other algorithms, showcasing the potential benefits of utilizing discovered multi-agent skills as complete units . The evaluation on MMM2, known as a super-hard task setting, highlights the superiority of VO-MASD-Hier over other algorithms, emphasizing its effectiveness in challenging scenarios .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of multi-agent skill discovery. Noteworthy researchers in this area include Jiayu Chen, Bhargav Ganguly, Tian Lan, and Vaneet Aggarwal . They have proposed novel auto-encoder schemes, such as VO-MASD-3D and VO-MASD-Hier, to capture subgroup- and temporal-level abstractions for forming multi-agent skills . The key to the solution mentioned in the paper involves developing encoder-decoder architectures and co-training them with a grouping function that dynamically groups agents to extract multi-agent skills effectively .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the discovered multi-agent skills in online Multi-Agent Reinforcement Learning (MARL) . The experiments were conducted on the StarCraft multi-agent challenge (SMAC), a benchmark for cooperative MARL, using extended task sets such as 'marine' and 'MMMs' . The skills were first discovered from offline trajectories of source tasks and then applied to each task in the task set, including both source and unseen tasks, for online MARL . The evaluation compared the skills discovered using different algorithms on the task sets to demonstrate the superiority of the multi-agent skills discovered by their methods . Additionally, the experiments aimed to show that hierarchical learning with skills discovered using their methods can significantly outperform traditional MARL algorithms, especially in tasks with sparse reward signals .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is DH, which is a multi-task offline dataset with trajectories segmented every H time steps . The code for the research project is open source and can be accessed in the released code folder, where the provided hyperparameter configurations are recommended for use .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The research conducted extensive experiments on multi-agent reinforcement learning (MARL) tasks, specifically focusing on skill discovery and its application in downstream MARL tasks . The experiments were carried out on the StarCraft multi-agent challenge (SMAC), a well-established benchmark for cooperative MARL, using extended task sets to evaluate the discovered multi-task multi-agent skills .

The paper demonstrated the effectiveness of the discovered multi-agent skills in online MARL tasks, showcasing superior performance compared to traditional MARL algorithms, especially in scenarios with sparse reward signals . By applying the skills discovered from offline trajectories to various tasks within the task sets, including source and unseen tasks, the research highlighted the practical utility of the discovered skills in enhancing MARL performance .

Furthermore, the experiments conducted in the paper involved comparing skills discovered using different algorithms on the task sets, emphasizing the superiority of the multi-agent skills discovered through their methods . The results indicated that hierarchical learning with the discovered skills significantly outperformed standard MARL algorithms, particularly in tasks with sparse reward signals .

Overall, the experiments and results presented in the paper provide strong empirical evidence supporting the scientific hypotheses related to multi-agent skill discovery and its application in improving performance in downstream MARL tasks, validating the effectiveness of the proposed methods in enhancing cooperative multi-agent reinforcement learning .


What are the contributions of this paper?

The paper "Variational Offline Multi-agent Skill Discovery" makes several key contributions:

  • Novel Auto-encoder Schemes: The paper proposes two innovative auto-encoder schemes, VO-MASD-3D and VO-MASD-Hier, to capture subgroup- and temporal-level abstractions simultaneously, enabling the formation of multi-agent skills .
  • Dynamic Grouping Function: An essential component of the proposed schemes is a dynamic grouping function that automatically detects latent subgroups based on agent interactions in a task, facilitating the extraction of coordination patterns in multi-agent scenarios .
  • Transferability of Discovered Skills: The method developed in the paper can be applied to offline multi-task data, allowing the discovered subgroup skills to be transferred across relevant tasks without the need for retraining, enhancing efficiency and performance in multi-agent reinforcement learning tasks .
  • Empirical Evaluations: Empirical evaluations conducted on StarCraft tasks demonstrate that the approach significantly outperforms existing methods in applying skills in multi-agent reinforcement learning. The discovered skills also prove effective in reducing learning difficulty in scenarios with delayed and sparse reward signals .

What work can be continued in depth?

To further advance the research in multi-agent reinforcement learning (MARL), a potential area for future exploration is the development of a co-training scheme involving the high-level policy and grouper for online MARL. This scheme aims to fine-tune the grouping function with task-specific rewards to enhance performance . Additionally, exploring the integration of discovered subgroup skills into the paradigm of centralized training with decentralized execution (CTDE) could be beneficial. This approach could leverage the stability of centralized training and the applicability of decentralized execution to improve the overall effectiveness of MARL algorithms .

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