Practical overview
When it fits
Use it when a project needs a code-level library for MDP or POMDP modeling, planning, or reinforcement-learning experimentation, and the team can work with C++ or its Python bindings.
Who it is for
- Developers implementing decision-making models or planning algorithms.
- Researchers and learners building reinforcement-learning or MDP/POMDP experiments in C++ or Python.
Documented uses
- Represent an MDP or POMDP in code or parse a supported subset of Cassandra-format model files.
- Apply supported algorithms, including the documented POMDP incremental-pruning example, to obtain a value function and policy.
- Run Python-driven experiments with native generative models that sample next states and rewards.
Requirements & limits
The public repository identifies the project as open source under the GPL-3.0 license.
- Python bindings offer a similar interface, but Python does not expose C++ templates; the documentation says custom implementations or wholly new approaches may require C++.
- The Cassandra parser supports a reasonable subset of the format, not necessarily every model file.
- Tutorials are written in C++, with equivalent Python code available in the examples folder.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Solve and simulate a POMDP in C++
Use the documented example pattern to solve a POMDP model and simulate actions over a finite horizon.
Before you start
- A POMDP model implementing the documented 10-method interface; the example uses makeTigerProblem().
- C++ access to the AI-Toolbox library and its POMDP classes.
Steps
Create a POMDP model and set a solution horizon, as in the example’s makeTigerProblem() and horizon = 10.
Construct an AIToolbox::POMDP::IncrementalPruning solver with the horizon and convergence parameter, then call it with the model to obtain the value function.
Construct a policy from the model dimensions and value function, and initialize a belief and sampled state.
Sample an action from the policy, then iterate through the horizon: sample the next state, observation, and reward from the model, and use each observation to select the next policy action.
Expected outcome
A value function and policy are used to simulate a finite-horizon POMDP interaction while accumulating reward.
Guide limitations
- This recipe follows the README example; it does not document build or installation steps.
- The model must meet the documented 10-method interface.
- The example uses a fixed finite horizon and does not specify a particular convergence guarantee.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- Svalorzen/AI-Toolbox · Official homepage title · retained in its source language