Researchers have developed a new method for autonomous agents to learn and reason about systems with hidden states, a problem often framed as learning a discrete Partially Observable Markov Decision Process (POMDP). The agent starts with knowledge of the POMDP's actions and observation spaces but must construct its state space, transitions, and observation models from sequential data. The proposed approach, building on Spectral methods like Predictive State Representations (PSRs), learns POMDP matrices up to a similarity transform estimated via tensor decomposition. This method allows for the generation of new plans for different goals and reward functions after the model has been learned, and it also demonstrates the impossibility of learning a POMDP beyond a partition of states from sequential data alone. AI
IMPACT Enables AI agents to learn and reason about systems with hidden states, potentially improving decision-making in complex environments.
RANK_REASON The cluster contains a research paper detailing a new method for learning POMDPs. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- DagsHub
- Gotit.pub
- Hugging Face
- partially observable Markov decision process
- Predictive State Representations
- ScienceCast
- Seiji Shaw
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