Comments (2)
Thanks for the report.
What's the difference between terminating during PO-UCT rollout versus having a reward model that only returns 0 reward after reaching the terminal state?
I didn't include that in the PO-UCT because it's not always the case that there is a terminal state in POMDPs (it's not part of the standard definition). You may have terminal states in some domains, like rock sample, but for domains like Tiger, the domain description explicitly says that after you open the door, the game resets (instead of terminates).
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If you desire the behavior of terminating rollout after reaching terminal state, I think right now you could create a child class of POUCT and implement this behavior for your domain. You can feel free to potentially contribute this class to this repo:)
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Related Issues (20)
- Using POMCP to solve a time-dependent problem HOT 4
- 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
- Does this library support continuous pomdp problems? HOT 1
- 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
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