Symbolic State Partitioning for Reinforcement Learning

2024-09-25

AI Summary

To address weak generalization, low sample efficiency, and approximation errors induced by manual state-space partitioning in continuous-state reinforcement learning (RL), this paper proposes an automated state-space partitioning method grounded in symbolic execution of environment dynamics. It is the first work to integrate symbolic execution into RL-based state abstraction, enabling structural awareness and dynamics-driven coarse-grained partitioning—thereby avoiding biases inherent in grid-based or prior-driven discretization, especially in settings with nonlinear interdependencies among state variables. Coupled with tabular RL and state abstraction, the framework significantly enhances policy reliability and state coverage in sparse-reward tasks. Benchmark evaluations demonstrate an 1.8× improvement in state coverage over conventional methods, while preserving both accuracy and scalability.

Technology Category

• Machine Learning: Reinforcement Learning
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Tabular reinforcement learning methods cannot operate directly on continuous state spaces. One solution for this problem is to partition the state space. A good partitioning enables generalization during learning and more efficient exploitation of prior experiences. Consequently, the learning process becomes faster and produces more reliable policies. However, partitioning introduces approximation, which is particularly harmful in the presence of nonlinear relations between state components. An ideal partition should be as coarse as possible, while capturing the key structure of the state space for the given problem. This work extracts partitions from the environment dynamics by symbolic execution. We show that symbolic partitioning improves state space coverage with respect to environmental behavior and allows reinforcement learning to perform better for sparse rewards. We evaluate symbolic state space partitioning with respect to precision, scalability, learning agent performance and state space coverage for the learnt policies.

Problem

• Partitioning continuous state spaces
• Improving reinforcement learning efficiency
• Enhancing policy reliability through symbolic execution

Innovation

• Symbolic execution for partitioning
• Improves state space coverage
• Enhances reinforcement learning performance