Research map / Learning theory and algorithms / Combinatorial and search optimization
Learning Features and Abstract Actions for Computing Generalized Plans
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use goal-relevant transitions in T_G rather than building the full T theory | Feature and abstract-action learning · Q_clear | CNF clauses: 59.6M -> 767K | Helped |
| Use goal-relevant transitions in T_G rather than building the full T theory | Feature and abstract-action learning · Q_on | CNF clauses: 25.8M -> 3.3M | Helped |
| Use goal-relevant transitions in T_G rather than building the full T theory | Feature and abstract-action learning · Q_gripper | CNF clauses: 4.7M -> 358K | Helped |
| Use goal-relevant transitions in T_G rather than building the full T theory | Feature and abstract-action learning · Q_reward | CNF clauses: 11.9M -> 1.2M | Helped |
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