How Action Abstractions Speed Up Reinforcement Learning Discovery

The Challenge of Long Trajectories#

Reinforcement learning agents and generative flow networks face mounting difficulty when policies must generate increasingly lengthy action sequences. As trajectories grow longer, two critical problems compound: determining which past actions deserve credit for eventual rewards becomes murkier, and systematically exploring the space of possible paths becomes computationally prohibitive. This challenge intensifies for entropy-seeking RL methods—particularly generative flow networks, which must learn to sample from complex distributions while uncovering multiple high-value states that each require dozens of steps to reach.

A New Approach to Action Abstraction#

To address these bottlenecks, researchers have developed a method that discovers and incorporates action abstractions directly into the learning process. The core idea is elegantly simple: the system scans successful high-reward trajectories for frequently reused action sequences, bundles those sequences into single "macro actions," and adds them to the agent's available action set. By treating multi-step patterns as atomic units, the agent can plan at a higher level of abstraction and navigate the reward landscape more efficiently.

Empirical Performance Gains#

Testing across both synthetic and real-world environments reveals tangible improvements in how quickly agents discover diverse high-reward objects, with particularly strong gains on harder exploration problems. Equally valuable is a secondary finding: the learned abstractions are themselves interpretable, naturally encoding the underlying structure of the environment's reward geometry. This property suggests the approach captures something fundamental about how the task decomposes.

Significance for Hierarchical Planning#

This work represents the first demonstration of hierarchical planning applied to amortized sequential sampling, bridging cognitive science insights about abstraction with practical RL challenges. By automating the discovery of task-relevant action hierarchies, the approach offers a promising path toward more sample-efficient learning in complex domains.