Inferring Probabilistic Reward Machines from Non-Markovian Reward Signals for Reinforcement Learning

Authors

  • Taylor Dohmen University of Colorado Boulder
  • Noah Topper University of Central Florida
  • George Atia University of Central Florida
  • Andre Beckus Air Force Research Laboratory
  • Ashutosh Trivedi University of Colorado Boulder
  • Alvaro Velasquez Air Force Research Laboratory

Keywords:

Reward Machines, Non-Markovian Rewards, Active Learning, Reinforcement Learning

Abstract

The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies. In recent years, the use of reward machines has relaxed this assumption by enabling a structured representation of non-Markovian rewards. In particular, such representations can be used to augment the state space of the underlying decision process, thereby facilitating non-Markovian reinforcement learning. However, these reward machines cannot capture the semantics of stochastic reward signals. In this paper, we make progress on this front by introducing probabilistic reward machines (PRMs) as a representation of non-Markovian stochastic rewards. We present an algorithm to learn PRMs from the underlying decision process and prove results around its correctness and convergence.

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Published

2022-06-13

How to Cite

Dohmen, T., Topper, N., Atia, G., Beckus, A., Trivedi, A., & Velasquez, A. (2022). Inferring Probabilistic Reward Machines from Non-Markovian Reward Signals for Reinforcement Learning. Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 574-582. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/19844