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AI Research
arXiv CS.AI·14d ago·5 min read
Toward Learning POMDPs Beyond Full-Rank Actions and State Observability
TL;DR
A novel approach enables autonomous agents to learn the parameters of a Partially Observable Markov Decision Process (POMDP) without full observability of states. This method leverages tensor decomposition to estimate transition and observation matrices, allowing for effective planning across different goals.
✦ Why It Matters
Implement tensor decomposition techniques to enhance your agent's learning capabilities in partially observable environments today.
Key Takeaways
- Utilize tensor decomposition for learning POMDP parameters.
- Focus on environments with hidden states for agent training.
- Leverage learned models for diverse planning tasks.
- Understand limitations in learning beyond state partitions.
How It Works
The method employs tensor decomposition to estimate the transition and observation matrices of a POMDP. By analyzing the rank conditions of these matrices, the approach allows agents to learn state representations that are sufficient for predicting future outcomes, even when the state space is not fully observable.