Comments (1)
The base classes are general enough to allow defining continuous POMDPs (see light dark example), but currently there is no solver for them -- I just added an item for this in call for contributions!
If your continuous domain has a discrete action space, you should be able to still apply POUCT but it may not work well (flat tree, for example).
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Related Issues (20)
- Benchmark w.r.t the original Silver's code? HOT 2
- SARSOP choose same action everytime. HOT 6
- Can't find module named 'pomdp_py.algorithms.po_uct' HOT 8
- Call for Contributions HOT 5
- Cannot convert type to pomdp_py.framework.basics.Action HOT 13
- Moving a problem from SARSOP to POMCP HOT 16
- Sarsop won't compute policy after changing observation model HOT 9
- Changing max_depth and planning_time for POMCP HOT 10
- Particle deprivation in POMCP at the end of the trial HOT 2
- Making a greedy rollout policy for POMCP HOT 8
- How to correctly modify a planner object HOT 9
- General question about goal and failure states? HOT 3
- How to correctly implement a goal/terminal state HOT 8
- Enable random seeding in POUCT / POMCP for deterministic behavior
- Transition and probability in Update belief of Multi Object Search HOT 2
- Multi object search history and tree construction HOT 3
- Modernize repo HOT 1
- Random.choice behavior change in python 3.9 HOT 4
- MOMDP representation? HOT 5
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