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AI Research
arXiv CS.AI·Jun 10·5 min read
Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes
TL;DR
Many decision-making tasks require understanding the entire history of actions rather than just the current state. QR-MAX is a new model-based reinforcement learning algorithm designed for discrete non-Markovian reward decision processes, which separates learning state transitions from reward handling.
✦ Why It Matters
Engineers can leverage QR-MAX for more efficient and robust decision-making in complex environments.
Key Takeaways
- Explore QR-MAX for tasks requiring historical context in decision-making.
- Utilize Bucket-QR-MAX for continuous state spaces without manual gridding.
- Leverage the factorization approach to improve sample efficiency.
- Test QR-MAX against existing RL methods to evaluate performance gains.
How It Works
QR-MAX separates the learning of state transitions from reward handling by using reward machines, which allows it to effectively manage non-Markovian rewards. This factorization leads to improved learning efficiency and guarantees optimal policy convergence.