Pushdown Reward Machines for Reinforcement Learning

arXiv — cs.LG•Thursday, November 13, 2025 at 5:00:00 AM

The recent introduction of Pushdown Reward Machines (pdRMs) represents a notable evolution in reinforcement learning (RL) methodologies. By building on the existing framework of reward machines, pdRMs enhance the ability to recognize and reward temporally extended behaviors, which are expressible in deterministic context-free languages. This increased expressiveness allows for more complex task representations, potentially leading to significant improvements in sample efficiency when paired with RL algorithms. The theoretical results provided in the study establish the expressive power of pdRMs, alongside space complexity considerations for the associated learning problems. Experimental results further demonstrate the practical implications of pdRMs, showing how agents can be effectively trained to perform tasks that were previously challenging. This advancement not only broadens the scope of RL applications but also sets the stage for future research in optimizing AI learning processe…

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