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technologyreview.com·3h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
TL;DR
Many Markov decision processes (MDPs) face challenges due to state-dependent action sets defined by operational constraints. Bellman-Taylor score decoding was developed to facilitate policy learning in a Euclidean score space while ensuring action feasibility through an action decoder.
✦ Why It Matters
Engineers can leverage Bellman-Taylor score decoding to improve decision-making in systems with complex action constraints.
Key Takeaways
- Explore Bellman-Taylor score decoding for complex MDPs.
- Utilize action decoders to maintain feasibility in DRL.
- Apply findings to queueing network control problems.
- Consider structural and algorithmic errors in performance evaluations.
Full Summary
Markov decision processes (MDPs) often involve feasible actions that depend on the current state, complicating the application of standard deep reinforcement learning (DRL) algorithms. To address this, Bellman-Taylor score decoding was introduced, which utilizes a Taylor expansion of the optimal action-value function to shift policy learning into a Euclidean score space.
This method incorporates an action decoder to maintain feasibility without requiring differentiation through the decoder. The resulting latent-score MDP can be optimized using conventional DRL techniques.
Performance guarantees indicate that the optimality gap can be broken down into structural approximation and algorithmic learning errors. In practical applications, such as queueing network control, the framework effectively learns a state-dependent dispatching rule, achieving near-optimal results in smaller instances and significant improvements in larger systems.
These findings suggest a promising direction for enhancing decision-making in complex operational environments.