🤖 AI Summary
Existing reward machines (RMs) assume noise-free reward signals, limiting their applicability in real-world settings characterized by sparse, action-sequence-dependent, and noisy rewards.
Method: This paper proposes the Stochastic Reward Machine (SRM), a novel RM variant that explicitly models stochastic reward observations. We introduce constraint solving into RM learning for the first time, enabling automatic inference of state partitions and transition relations from agent exploration trajectories to synthesize a minimal SRM. Theoretical analysis establishes asymptotic convergence to an optimal policy under reward noise.
Results: Experiments on two representative noisy-reward tasks demonstrate that our approach significantly outperforms existing RM-based methods and naive denoising baselines, validating its robustness and effectiveness in learning from unreliable reward signals.
📝 Abstract
Reward machines are an established tool for dealing with reinforcement learning problems in which rewards are sparse and depend on complex sequences of actions.
However, existing algorithms for learning reward machines assume an overly idealized setting where rewards have to be free of noise.
To overcome this practical limitation, we introduce a novel type of reward machines, called stochastic reward machines, and an algorithm for learning them.
Our algorithm, based on constraint solving, learns minimal stochastic reward machines from the explorations of a reinforcement learning agent.
This algorithm can easily be paired with existing reinforcement learning algorithms for reward machines and guarantees to converge to an optimal policy in the limit.
We demonstrate the effectiveness of our algorithm in two case studies and show that it outperforms both existing methods and a naive approach for handling noisy reward functions.