Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
The Dually Flat Geometry of Planning as Inference
TL;DR
A new approach to reinforcement learning is introduced, embedding planning into dynamics via a resetting process. This leads to a dually flat statistical manifold that enhances decision-making and generalizes planning from linear to nonlinear rewards.
✦ Why It Matters
Explore the implications of dually flat geometry in your reinforcement learning models today.
Key Takeaways
- Integrate planning into your reinforcement learning dynamics.
- Utilize visitation measures for enhanced decision-making.
- Apply natural-gradient steps for solving iterations.
- Consider nonlinear functionals of visitation in your models.
How It Works
The approach embeds the planning criterion into the dynamics of the system through a resetting process, allowing for a new characterization of the occupancy measure. This leads to the definition of a visitation measure, which serves as a basis for the information geometry of decision-making.
The dually flat statistical manifold formed by visitation probabilities and log-policies enables the generalization of planning from linear to nonlinear rewards, enhancing the flexibility and applicability of reinforcement learning.