Research map / Multi-agent systems and games / Games and equilibria
Open-ended learning in symmetric zero-sum games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use rectified Nash response (α-RN) instead of self-play, uniform response, or Nash response | Agent populations · Colonel Blotto | relative population performance | Helped |
| Use rectified Nash response (α-RN) instead of self-play, uniform response, or Nash response | Agent populations · Differentiable Lotto | relative population performance | Helped |
| Track all agents produced by self-play rather than only its final agent | Self-play · Differentiable Lotto | relative population performance | Mixed |
| Track all agents produced by self-play rather than only its final agent | Self-play · Colonel Blotto | relative population performance | Helped |
| Train effective agents against opponents they lose to instead of opponents they beat or tie (ablation) | Rectified Nash response · Disc game | gamescape size | Hurt |
| Use rectified Nash response rather than other algorithms | Agent populations · Differentiable Lotto | gamescape area | Helped |
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