Research map / Multi-agent systems and games / Games and equilibria
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use level-1 recursive opponent modeling with a learned conditional opponent policy | PR2-Q · Iterated matrix game | equilibrium convergence | Helped |
| Use level-1 recursive opponent modeling with a learned conditional opponent policy | PR2-AC · Max of Two Quadratic Game | equilibrium reached | Helped |
| Use level-1 recursive opponent modeling with a learned conditional opponent policy | PR2-AC · Particle World cooperative navigation | normalized score | Helped |
| Use level-1 recursive opponent modeling with a learned conditional opponent policy | PR2-AC · Particle World competitive tasks | normalized advantage score | Mixed |
| Use level-1 recursive opponent modeling with a learned conditional opponent policy | PR2-AC · Particle World physical deception | normalized advantage score | Hurt |
| Pair PR2-AC with an opponent that models other agents | PR2-AC · Max of Two Quadratic Game | equilibrium reached | Mixed |
| Use MASQL instead of PR2-AC | Max of Two Quadratic Game · Max of Two Quadratic Game | global equilibrium reached | Mixed |
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