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Author Mathis Delsart
Last update May 26, 2026
Map basesWorkers16x16A
Evaluation deterministic policy
Opponents
18
Full tournament
Games shown
36
Both starting positions
Wins
35
1 loss (WorkerRush, P0)
Win rate
97.2%
Deterministic play
This page hosts the supplementary material for the Work-in-Progress paper on competitive single-map MicroRTS agents. It includes (i) a summary of the final tournament on basesWorkers16x16A (final standings and head-to-head matrix), (ii) one recorded game per starting position (P0 and P1) for each of the 18 opponents in the tournament, and (iii) detailed game-theoretic metrics (Nash, Alpha-Rank, Copeland, robustness) behind the ranking.
Contents

Tournament summary

Overall ranking of our agent against every opponent on the final 16×16 single-map tournament. The two figures below give the context that is referenced throughout the game recordings. More detailed game-theoretic analyses are provided at the bottom of the page.

Final standings
Final standings across all opponents.
Head-to-head matrix
Head-to-head win-rate matrix.

Example games

Recordings below show our single-map agent (UECD-Best) playing one game per starting position against each opponent of the tournament, on the basesWorkers16x16A map. All games are played with deterministic actions (argmax over the policy), matching the tournament protocol. Left column: our agent starts in position P0 (top-left corner). Right column: our agent starts in position P1 (bottom-right corner). Opponents are listed in the tournament order of strength.

vs RAISocketAI source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs UtsImass source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs TMA source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs UECD-TopFeats source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs ObiBotKenobi source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs UECD-AllFeats source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs UECD-Rushed source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs CoacAI source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs WorkerRush source ↗

Our agent plays in P0 positionLoss
Our agent plays in P1 positionWin

vs Mayari source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs MixedBot source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs Droplet source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs GridNet source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs Tiamat source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs StrategyTactics source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs NaiveMCTS source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs LightRush source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

vs RandomBiasedAI source ↗

Our agent plays in P0 positionWin
Our agent plays in P1 positionWin

Detailed tournament analysis

Game-theoretic metrics behind the final standings. These complement the head-to-head matrix shown at the top of the page.

Copeland scores
Copeland scores.
Alpha-Rank sweep
Alpha-Rank sweep.
Nash scores
Nash averaging scores.
Robustness score
Robustness across opponents.