A Compression-Inspired Framework for Macro Discovery

Reinforcement learning has seen rapid development, with techniques like options and proto-value functions enhancing exploration and transfer. However, these methods often depend on explicit state structures or predefined subgoals, limiting their generality. Recent efforts like Eigen-Options and Option-Critic have improved automatic skill discovery but still face challenges in scalability and environment diversity. Compression algorithms such as LZW, traditionally used in data compression, offer a promising avenue for extracting recurring patterns without relying on explicit state representations. Combining these insights, the current work aims to develop a universal, model-free macro discovery framework capable of capturing high-frequency behaviors across tasks, thereby improving transfer learning and exploration efficiency.

The core challenge is to automatically identify reusable, high-frequency action sequences—macros—that can generalize across related tasks without relying on environment-specific structures. Existing methods often depend on bottleneck states or predefined subgoals, which are not always available or effective in complex, high-dimensional spaces. The difficulty lies in balancing macro utility and diversity, ensuring that the macro set covers the task space sufficiently without becoming overly large or redundant. Addressing this problem is crucial for scalable transfer learning, autonomous skill acquisition, and efficient exploration in diverse environments, especially when explicit state information is limited or unavailable.

1) Leveraging trajectory compression (LZW) to automatically generate candidate macros, bypassing the need for explicit structural assumptions. 2) Introducing the U-value metric, which estimates macro utility based on expected Q-values, providing a principled evaluation criterion. 3) Employing a KL divergence-based diversity filter to select macros that are behaviorally distinct, ensuring broad coverage of the task space. 4) Deriving a closed-form macro value formula under greedy policies, enabling efficient evaluation without extensive sampling. These innovations collectively enable scalable, task-agnostic macro discovery, significantly enhancing transfer learning and exploration in RL.

• �� Sample trajectories from near-optimal policies across training tasks. • Apply LZW compression to these trajectories, encoding frequent action sequences as macros. • Compute macro U-values using the derived closed-form formula based on known Q-values. • Rank macros by U-value, then filter for diversity using KL divergence between their end-state distributions. • Select macros that maximize utility and diversity, forming a compact macro set. • Incorporate selected macros into the action space and retrain RL agents. • Evaluate performance improvements in unseen tasks, comparing against primitive-only baselines. This pipeline is fully automated, task-agnostic, and scalable, enabling efficient macro discovery without environment-specific assumptions.

Experiments involved maze navigation and chain tasks with known transition models, allowing precise macro value computation. The agents trained with Q-learning and DQN compared primitive-only policies against macro-augmented policies. Metrics included average reward, convergence speed, macro diversity, and transfer performance. Ablation studies tested the impact of each component—generation, evaluation, and filtering—showing that each stage significantly contributed to overall gains. Additional tests in continuous spaces used neural networks for Q-value approximation, confirming the framework’s adaptability. Results consistently demonstrated that macro-based policies learned faster, achieved higher rewards, and generalized better across tasks.

Macros improved learning speed by up to 50% in maze and chain tasks, with reward increases over 20%. The diversity filtering effectively prevented macro redundancy, ensuring broad behavioral coverage. Compared to Eigen-Options and Option-Critic, the proposed method showed superior transferability across different transition structures. The macro value formula closely matched empirical performance, validating the theoretical foundation. Ablation results confirmed each component’s importance, with the full pipeline outperforming variants. These findings establish the framework’s robustness, scalability, and effectiveness in diverse RL settings.

Immediate applications include robotic navigation, autonomous resource management, and complex control tasks where sample efficiency is critical. The framework requires only trajectory data, making it suitable for real-world scenarios with limited environment models. Long-term, it could enable lifelong learning systems capable of autonomously accumulating and reusing skills across a wide range of environments, reducing training costs and improving adaptability in robotics, industrial automation, and AI-driven decision systems.

Dependence on high-quality trajectory samples from near-optimal policies may limit applicability in environments where such data is costly. The open-loop macro design ignores state information, which could hinder performance in highly context-dependent tasks. The closed-form macro value computation assumes known Q-values, requiring prior training or approximation, which may introduce errors. Scalability to very high-dimensional or continuous spaces remains challenging, necessitating integration with deep learning techniques for feature extraction and value estimation. Future work should address macro adaptation in dynamic environments and hierarchical macro structures.