Active Reward Machine Inference From Raw State Trajectories
Reward machines are automaton-like structures that capture the memory required to accomplish a multi-stage task. When combined with reinforcement learning or optimal control methods, they can be used to synthesize robot policies to achieve such tasks. However, specifying a reward machine by hand, including a labeling function capturing high-level features that the decisions are based on, can be a daunting task. This paper deals with the problem of learning reward machines directly from raw state and policy information. As opposed to existing works, we assume no access to observations of rewards, labels, or machine nodes, and show what trajectory data is sufficient for learning the reward ...
| Authors | Mohamad Louai Shehab, Antoine Aspeel, Necmiye Ozay |
|---|---|
| Primary category | cs.RO |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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