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Multi-level meta-reinforcement learning with skill-based curriculum

arXiv:2603.08773v1 Announce Type: new Abstract: We consider problems in sequential decision making with natural multi-level structure, where sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure has remained a longstanding challenge; we describe an efficient multi-level procedure for repeatedly compressing Markov decision processes (MDPs), wherein a parametric family of policies at one level is treated as single actions in the compressed MDPs at higher levels, while preserving the semantic meanings and structure of the original MDP, and mimicking the natural logic to address a complex MDP. Higher-level MDPs are themselves independent MDPs with less stochasticity, and may be solved using existing algorithms. As a byproduct, spatial or temporal scales may be coarsened at higher levels, making it more efficient to find long-term optimal policies. The multi-level representation delivered by this procedure decouples sub

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Sichen Yang (Johns Hopkins University), Mauro Maggioni (Johns Hopkins University) · · 1 min read · 17 views
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arXiv:2603.08773v1 Announce Type: new Abstract: We consider problems in sequential decision making with natural multi-level structure, where sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure has remained a longstanding challenge; we describe an efficient multi-level procedure for repeatedly compressing Markov decision processes (MDPs), wherein a parametric family of policies at one level is treated as single actions in the compressed MDPs at higher levels, while preserving the semantic meanings and structure of the original MDP, and mimicking the natural logic to address a complex MDP. Higher-level MDPs are themselves independent MDPs with less stochasticity, and may be solved using existing algorithms. As a byproduct, spatial or temporal scales may be coarsened at higher levels, making it more efficient to find long-term optimal policies. The multi-level representation delivered by this procedure decouples sub-tasks from each other and usually greatly reduces unnecessary stochasticity and the policy search space, leading to fewer iterations and computations when solving the MDPs. A second fundamental aspect of this work is that these multi-level decompositions plus the factorization of policies into embeddings (problem-specific) and skills (including higher-order functions) yield new transfer opportunities of skills across different problems and different levels. This whole process is framed within curriculum learning, wherein a teacher organizes the student agent's learning process in a way that gradually increases the difficulty of tasks and and promotes transfer across MDPs and levels within and across curricula. The consistency of this framework and its benefits can be guaranteed under mild assumptions. We demonstrate abstraction, transferability, and curriculum learning in examples, including MazeBase+, a more complex variant of the MazeBase example.

Executive Summary

This article proposes a novel approach to sequential decision-making problems with natural multi-level structure, referred to as multi-level meta-reinforcement learning with skill-based curriculum. The authors introduce an efficient multi-level procedure for compressing Markov decision processes (MDPs) while preserving their semantic meanings and structure. This procedure enables the decoupling of sub-tasks, reduction of unnecessary stochasticity, and policy search space, ultimately leading to fewer iterations and computations. The framework also facilitates transfer opportunities across different problems and levels through the factorization of policies into embeddings and skills. The consistency of this framework and its benefits are guaranteed under mild assumptions. The authors demonstrate the effectiveness of their approach through examples, including MazeBase+, a complex variant of the MazeBase example. This work has significant implications for the field of reinforcement learning and decision-making.

Key Points

  • ▸ Efficient multi-level procedure for compressing Markov decision processes (MDPs)
  • ▸ Decoupling of sub-tasks and reduction of unnecessary stochasticity and policy search space
  • ▸ Facilitation of transfer opportunities across different problems and levels through policy factorization

Merits

Strength in Problem Representation

The proposed approach systematically infers and leverages the hierarchical structure of sequential decision-making problems, enabling more efficient and effective problem solving.

Transfer Learning Opportunities

The framework facilitates transfer opportunities across different problems and levels, enabling the reuse of learned skills and knowledge.

Efficient Computation

The multi-level representation and factorization of policies into embeddings and skills reduce the computational complexity of solving MDPs.

Demerits

Assumption of Mild Conditions

The consistency of the framework and its benefits are guaranteed under mild assumptions, which may not always be met in real-world scenarios.

Complexity of Policy Factorization

The factorization of policies into embeddings and skills may add complexity to the problem-solving process, particularly for large and complex MDPs.

Expert Commentary

This article makes a significant contribution to the field of reinforcement learning by proposing a novel approach to sequential decision-making problems with natural multi-level structure. The authors' efficient multi-level procedure for compressing Markov decision processes (MDPs) and factorization of policies into embeddings and skills are particularly noteworthy. The framework's facilitation of transfer learning opportunities and efficient computation has significant implications for the development of more adaptive and responsive decision-making systems. However, the assumption of mild conditions and complexity of policy factorization are potential limitations that need to be addressed. Overall, this work has the potential to significantly impact the field of reinforcement learning and decision-making.

Recommendations

  • ✓ Future research should explore the application of this framework to more complex and real-world scenarios, such as robotics and autonomous vehicles.
  • ✓ The authors should investigate methods to further reduce the complexity of policy factorization and address the assumption of mild conditions.

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