coorl-lost-cities/docs/deep-cfr-evaluation-profile-2026-05-07.md

Deep CFR Evaluation Profile 2026-05-07

Run directory:

/mnt/2tbhdd/coolrl-lost-cities-runs/2026-05-07_030634_deep_cfr_profile_eval_breakdown_10iter

Command:

uv run python -m coolrl_lost_cities.games.classic.deep_cfr.cli train \
  --config configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml \
  --checkpoint-dir /mnt/2tbhdd/coolrl-lost-cities-runs/2026-05-07_030634_deep_cfr_profile_eval_breakdown_10iter \
  --max-iterations 10 \
  --save-latest-only

Summary

The run completed 10 iterations. Loss values stayed finite.

Evaluation ran on iterations 5 and 10.

Iteration	iteration_seconds	evaluation_seconds
5	16.473433	11.055458
10	16.835262	10.855797

Opponent Averages

Values below are averaged across iterations 5 and 10.

Opponent	elapsed	avg len	policy select	network	postprocess	encoding	legal mask	opponent act
safe_heuristic_strict	2.184393	158.13	1.628546	1.279717	0.175923	0.043837	0.038600	0.518953
random	2.072657	186.11	1.918007	1.515231	0.207319	0.051491	0.037316	0.112155
safe_heuristic	1.942907	143.23	1.471839	1.155444	0.159592	0.039683	0.035180	0.435870
noisy_safe	1.904932	143.71	1.493521	1.174909	0.161256	0.039845	0.034331	0.374162
safe_heuristic_loose	1.829347	136.13	1.400569	1.099200	0.151752	0.037722	0.033497	0.394891
passive_discard	1.014156	96.82	0.982162	0.774097	0.105579	0.026702	0.021929	0.006383

Other averaged step costs were small:

Opponent	apply action	diagnostics	final scoring
safe_heuristic_strict	0.005185	0.008161	0.000569
random	0.006229	0.009001	0.000632
safe_heuristic	0.004748	0.007636	0.000581
noisy_safe	0.004818	0.007761	0.000584
safe_heuristic_loose	0.004473	0.007438	0.000592
passive_discard	0.002911	0.005156	0.000477

Notes

policy_select_seconds dominated every opponent.

Inside policy selection, policy_network_seconds was the largest component. policy_postprocess_seconds was second. policy_encoding_seconds and policy_legal_mask_seconds were much smaller.

opponent_act_seconds was meaningful for safe heuristic opponents, but was still smaller than policy network time.

apply_action_seconds, diagnostics_seconds, and final_scoring_seconds were small in this run.