Support for state-dependent action spaces
- Dominant language
- PDDL
- Stars
- 39
- Forks
- 19
- PR merge metrics
- No merged PRs in 30d
Description
Currently, there is no way to specify that certain actions are only possible in certain states.
Clearly one could just give a large penalty, but providing a way to model A as a function of current state s would definitely simplify modeling and allow solvers to exploit this feature.
One particular use-case would be controlling multiple (possibly conditional) parameters. In many cases it makes sense to model this such that at every step, only the value of a single parameter is changed, i.e. the actions would be the alternative values for the parameter to be set.
Contributor guide
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Research direction
The issue names no files, tests, or entry points. Start by locating the environment and action-space APIs, then trace how solvers consume available actions; done should allow valid actions to depend on the current state and support conditional parameter changes without penalty workarounds.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100