Belief Embedding Tree Search
Beliefs in a Partially Observable Markov Decision Process (POMDP) are posterior distributions over the hidden state given the agent's history of actions and observations. They summarize everything the history says about the state, so planning in a POMDP is planning in a Markov decision process (MDP) over beliefs. Belief MDPs have continuous state spaces even for finite POMDPs, and exact planning in them is intractable in general. Planners must therefore approximate beliefs and estimate values. Monte Carlo planners do both inside a search tree, with an approximate belief at every node and a value estimate at every leaf. Approximations usually consist of sets of sampled states, with leaf va...
| Authors | Christopher Solinas, Radovan Haluška, Martin Schmid et al. |
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| Primary category | cs.AI |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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