Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Interactive Fiction Games: A Colossal Adventure
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use choice-based DRRN instead of template-based TDQN | Single-game agents on Jericho games · Jericho 32 games | completion: 6.1% -> 10.7% | Helped |
| Train a single-game agent rather than use the general-game NAIL agent | Interactive fiction games · Jericho 32 games | completion: 4.9% -> 6.1%; 4.9% -> 10.7% | Mixed |
| Use the template-based TDQN rather than uniform canonical-action sampling | Interactive fiction games · Jericho 32 games | completion: 1.8% -> 6.1% | Helped |
| Use DRRN rather than TDQN | Single-game agents · Acorncourt | raw score: 1.6 -> 10 | Helped |
| Use TDQN rather than DRRN | Single-game agents · Omniquest | raw score: 5 -> 16.8 | Helped |
| Use DRRN rather than TDQN | Single-game agents · Zork1 | raw score: 9.9 -> 32.6 | Helped |
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