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Clean up Deep CFR config schema

Browse Source 
Remove legacy aliases, rename max_hours to max_minutes, collapse the
four checkpoint save flags into save_every + save_latest, and change
defaults to safer values (opponent_policy=self_play_league,
device=auto, eval_every=50, max_depth=null). Migrate all archived
yaml configs and tests to the new schema.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
coolguy
2026-05-07 16:09:19 +09:00
co-authored by Claude Opus 4.7
parent c2b88d7c9c
commit a177031963
21 changed files with 257 additions and 367 deletions
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AGENTS.md
+12 -16
View File
@@ -66,11 +66,9 @@ Short fixed-iteration run:
```bash
uv run lost-cities-deep-cfr train \
--config configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml \
--set run.iterations=100 \
--set run.max_hours=null \
--set run.max_iterations=null \
--set checkpoint.save_latest_only=true \
--set checkpoint.save_every_iteration=false
--set run.max_iterations=100 \
--set run.max_minutes=null \
--set checkpoint.save_every=0
```
Use explicit run directories for experiments. Put Deep CFR runs under
@@ -95,17 +93,16 @@ Useful train controls:
- `--resume PATH`: resume from a specific checkpoint.
- `--set PATH=VALUE`: override config fields. It is repeatable and parses
values as YAML, e.g. `--set traversal.num_workers=4` or
`--set run.max_hours=null`.
`--set run.max_minutes=null`.
Common `--set` overrides:
- `--set run.device=cuda`: set the trainer device.
- `--set checkpoint.exact_resume=true`: require checkpoint config compatibility.
- `--set checkpoint.save_latest=false --set checkpoint.save_every_iteration=false
--set checkpoint.save_iteration_interval=0`: disable checkpoint writes.
- `--set checkpoint.save_latest_only=true --set checkpoint.save_every_iteration=false`:
keep only `latest.pt`.
- `--set checkpoint.save_iteration_interval=N`: archive every N iterations.
- `--set checkpoint.save_latest=false --set checkpoint.save_every=0`:
disable checkpoint writes.
- `--set checkpoint.save_every=0`: keep only `latest.pt` (no archives).
- `--set checkpoint.save_every=N`: archive every N iterations.
## Long Runs
@@ -149,16 +146,15 @@ The unbounded config intentionally has:
```yaml
run:
iterations: null
max_iterations: null
max_hours: null
max_minutes: null
checkpoint:
save_iteration_interval: 100
save_every: 100
```
`latest.pt` is updated continuously; archive checkpoints are written every 100
iterations. If disk is tight, prefer `--set checkpoint.save_latest_only=true` or
increase `save_iteration_interval`.
iterations. If disk is tight, set `--set checkpoint.save_every=0` (keep only
`latest.pt`) or increase `save_every`.
## Evaluation And Analysis
configs/deep_cfr/deep_cfr_color_shared_512x3.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_color_shared_512x3
iterations: null
seed: 42
max_iterations: 10000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -30,10 +29,10 @@ network:
color_attention_heads: 4
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -49,7 +48,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -71,9 +70,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 512
@@ -89,9 +87,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_color_shared_512x3
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_color_shared_attention_512x3.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_color_shared_attention_512x3
iterations: null
seed: 42
max_iterations: 10000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -30,10 +29,10 @@ network:
color_attention_heads: 8
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -49,7 +48,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -71,9 +70,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 512
@@ -89,9 +87,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_color_shared_attention_512x3
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_color_shared_attention_exp_1000iter.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_color_shared_attention_1000iter
iterations: null
seed: 42
max_iterations: 1000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -30,10 +29,10 @@ network:
color_attention_heads: 8
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -49,7 +48,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -71,9 +70,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 256
@@ -89,9 +87,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_color_shared_attention_exp_1000iter
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_opponent_average_strategy_512x3_1000iter.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_opponent_average_strategy_512x3_1000iter
iterations: null
seed: 79
max_iterations: 1000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -27,10 +26,10 @@ network:
activation: relu
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_opponent_average_strategy_512x3_1000iter
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_opponent_network_1024x4_1000iter.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_opponent_network_1024x4_1000iter
iterations: null
seed: 79
max_iterations: 1000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -27,10 +26,10 @@ network:
activation: relu
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_opponent_network_1024x4_1000iter
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_opponent_network_512x3_1000iter.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_opponent_network_512x3_1000iter
iterations: null
seed: 79
max_iterations: 1000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -27,10 +26,10 @@ network:
activation: relu
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_opponent_network_512x3_1000iter
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_pure_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_pure_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter
iterations: null
seed: 79
max_iterations: 10000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -27,10 +26,10 @@ network:
activation: relu
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_pure_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_selfplay_anchor_safe_512x3_2x_updates_10000iter.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_selfplay_anchor_safe_512x3_2x_updates_10000iter
iterations: null
seed: 79
max_iterations: 10000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -27,10 +26,10 @@ network:
activation: relu
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_selfplay_anchor_safe_512x3_2x_updates_10000iter
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml
+3 -5
View File
@@ -2,7 +2,7 @@ run:
experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability
seed: 79
max_iterations: null
max_hours: 4
max_minutes: 240
device: cuda
use_amp: false
@@ -39,7 +39,7 @@ traversal:
cutoff_rollout_max_steps: 300
progress_every_traversals: 10
num_workers: 8
traversal_worker_chunk_size: 8
worker_chunk_size: 8
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
outcome_sampling_value_clip: 500
@@ -87,7 +87,5 @@ evaluation:
checkpoint:
directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability
save_every_iteration: false
save_iteration_interval: 10
save_latest_only: false
save_every: 10
progress_interval_seconds: 20.0
configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter
iterations: null
seed: 79
max_iterations: 10000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -27,10 +26,10 @@ network:
activation: relu
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_lcfr_10000iter.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability_512x3_lcfr_10000iter
iterations: null
seed: 79
max_iterations: 10000
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -27,10 +26,10 @@ network:
activation: relu
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -69,9 +68,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 256
@@ -87,9 +85,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_lcfr_10000iter
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_unbounded.yaml
+7 -11
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability_512x3_unbounded
iterations: null
seed: 79
max_iterations: null
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -27,10 +26,10 @@ network:
activation: relu
traversal:
traversals_per_iteration: 2
traversals_per_player: 2
traversals_per_player: 70
max_depth: null
max_nodes: 10000
max_nodes_per_traversal: 10000
max_nodes_per_traversal: 1000
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
store_strategy_on_opponent_nodes: false
num_workers: 8
worker_chunk_size: 4
traversal_worker_chunk_size: 8
worker_chunk_size: 8
progress_every_traversals: 10
endpoint_depth_bucket_width: 100
endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
recent_window: 5
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 32
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
advantage_batch_size: 1024
strategy_batch_size: 1024
advantage_updates_per_iteration: 256
@@ -86,9 +84,7 @@ memory:
checkpoint:
directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_unbounded
save_latest: true
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
exact_resume: false
configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded.yaml
+3 -6
View File
@@ -1,9 +1,8 @@
run:
experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability_unbounded
iterations: null
seed: 79
max_iterations: null
max_hours: null
max_minutes: null
device: cuda
use_amp: false
@@ -40,7 +39,7 @@ traversal:
cutoff_rollout_max_steps: 300
progress_every_traversals: 10
num_workers: 8
traversal_worker_chunk_size: 8
worker_chunk_size: 8
regret_matching_epsilon: 0.0001
outcome_sampling_epsilon: 0.2
outcome_sampling_value_clip: 500
@@ -88,7 +87,5 @@ evaluation:
checkpoint:
directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded
save_every_iteration: false
save_iteration_interval: 100
save_latest_only: false
save_every: 100
progress_interval_seconds: 20.0
configs/deep_cfr/smoke.yaml
+9 -7
View File
@@ -1,5 +1,5 @@
run:
iterations: 1
max_iterations: 1
seed: 1
device: cpu
@@ -7,16 +7,17 @@ network:
hidden_size: 16
traversal:
traversals_per_iteration: 1
traversals_per_player: 1
max_depth: 2
max_nodes: 64
max_nodes_per_traversal: 64
num_workers: 0
worker_chunk_size: 1
optimization:
advantage_train_steps: 1
strategy_train_steps: 1
batch_size: 2
advantage_batch_size: 2
strategy_batch_size: 2
advantage_updates_per_iteration: 1
strategy_updates_per_iteration: 1
learning_rate: 0.001
memory:
@@ -25,7 +26,8 @@ memory:
checkpoint:
directory: runs/deep_cfr/smoke
save_every_iteration: false
save_every: 0
save_latest: false
evaluation:
eval_every: 0
src/coolrl_lost_cities/games/classic/deep_cfr/benchmark.py
+5 -4
View File
@@ -13,14 +13,15 @@ def benchmark_traversal(
) -> dict[str, float | int]:
base = config or DeepCFRConfig.model_validate(
{
"run": {"iterations": 1},
"traversal": {"traversals_per_iteration": 8, "max_depth": 4},
"checkpoint": {"save_every_iteration": False},
"run": {"max_iterations": 1},
"traversal": {"traversals_per_player": 8, "max_depth": 4},
"checkpoint": {"save_every": 0, "save_latest": False},
}
)
data = base.model_dump(mode="python")
data["traversal"]["num_workers"] = num_workers
data["checkpoint"]["save_every_iteration"] = False
data["checkpoint"]["save_every"] = 0
data["checkpoint"]["save_latest"] = False
data["evaluation"]["eval_every"] = 0
cfg = DeepCFRConfig.model_validate(data)
trainer = DeepCFRTrainer(cfg, cfg.rules.to_lost_cities_config(seed=cfg.run.seed))
src/coolrl_lost_cities/games/classic/deep_cfr/cli.py
+3 -3
View File
@@ -136,10 +136,10 @@ def benchmark_command(args: argparse.Namespace) -> None:
{
"run": {"seed": args.seed},
"traversal": {
"traversals_per_iteration": args.traversals,
"traversals_per_player": args.traversals,
"max_depth": args.depth,
},
"checkpoint": {"save_every_iteration": False},
"checkpoint": {"save_every": 0},
}
)
if args.compare:
@@ -212,7 +212,7 @@ def main(argv: list[str] | None = None) -> None:
metavar="PATH=VALUE",
help=(
"Override config fields using dotted paths. Repeatable. VALUE is parsed as "
"YAML, e.g. --set traversal.num_workers=4 --set run.max_hours=null."
"YAML, e.g. --set traversal.num_workers=4 --set run.max_minutes=null."
),
)
train.add_argument(
src/coolrl_lost_cities/games/classic/deep_cfr/config.py
+15 -66
View File
@@ -18,23 +18,18 @@ class StrictModel(BaseModel):
class RunConfig(StrictModel):
experiment_name: str = "deep_cfr"
iterations: int | None = 1
max_iterations: int | None = None
max_hours: float | None = None
max_minutes: float | None = None
seed: int = 1
device: str = "cpu"
device: str = "auto"
use_amp: bool = False
@field_validator("device")
@classmethod
def _normalize_device(cls, value: str) -> str:
token = value.strip().lower()
if token == "cuda":
return "cuda"
if token == "cpu":
return "cpu"
if token == "auto":
return "auto"
if token in {"cuda", "cpu", "auto"}:
return token
return token
@@ -95,12 +90,10 @@ class NetworkConfig(StrictModel):
class TraversalConfig(StrictModel):
traversals_per_iteration: int = 2
traversals_per_player: int | None = None
traversals_per_player: int = 8
sampling_mode: str = "outcome"
max_depth: int | None = 8
max_nodes: int | None = 10_000
max_nodes_per_traversal: int | None = None
max_depth: int | None = None
max_nodes_per_traversal: int | None = 10_000
regret_matching_epsilon: float = 1.0e-8
outcome_sampling_epsilon: float = 0.0
outcome_sampling_value_clip: float | None = None
@@ -109,13 +102,12 @@ class TraversalConfig(StrictModel):
cutoff_rollouts: int = 0
cutoff_rollout_policy: str = "random"
cutoff_rollout_max_steps: int = 10_000
opponent_policy: str = "network"
opponent_policy: str = "self_play_league"
strategy_sample_interval: int = 1
store_strategy_on_traverser_nodes: bool = True
store_strategy_on_opponent_nodes: bool = True
num_workers: int | str = 0
worker_chunk_size: int = 4
traversal_worker_chunk_size: int | None = None
progress_every_traversals: int = 0
endpoint_depth_bucket_width: int = 100
endpoint_depth_bucket_max: int = 1000
@@ -169,21 +161,6 @@ class TraversalConfig(StrictModel):
workers = max(0, int(self.num_workers))
return min(workers, batches) if batches is not None and batches > 0 else workers
def resolved_traversals_per_player(self) -> int:
if self.traversals_per_player is not None:
return max(0, int(self.traversals_per_player))
return max(0, int(self.traversals_per_iteration))
def resolved_max_nodes(self) -> int | None:
if self.max_nodes_per_traversal is not None:
return self.max_nodes_per_traversal
return self.max_nodes
def resolved_worker_chunk_size(self) -> int:
if self.traversal_worker_chunk_size is not None:
return max(1, int(self.traversal_worker_chunk_size))
return max(1, int(self.worker_chunk_size))
class RegretMatchingConfig(StrictModel):
all_negative_fallback: str = "uniform"
@@ -225,37 +202,14 @@ class SelfPlayLeagueConfig(StrictModel):
class OptimizationConfig(StrictModel):
advantage_train_steps: int = 1
strategy_train_steps: int = 1
batch_size: int = 32
advantage_batch_size: int | None = None
strategy_batch_size: int | None = None
advantage_updates_per_iteration: int | None = None
strategy_updates_per_iteration: int | None = None
advantage_batch_size: int = 256
strategy_batch_size: int = 256
advantage_updates_per_iteration: int = 64
strategy_updates_per_iteration: int = 64
learning_rate: float = 1.0e-3
weight_decay: float = 0.0
grad_clip: float = 0.0
def resolved_advantage_batch_size(self) -> int:
if self.advantage_batch_size is not None:
return max(1, int(self.advantage_batch_size))
return max(1, int(self.batch_size))
def resolved_strategy_batch_size(self) -> int:
if self.strategy_batch_size is not None:
return max(1, int(self.strategy_batch_size))
return max(1, int(self.batch_size))
def resolved_advantage_train_steps(self) -> int:
if self.advantage_updates_per_iteration is not None:
return max(0, int(self.advantage_updates_per_iteration))
return max(0, int(self.advantage_train_steps))
def resolved_strategy_train_steps(self) -> int:
if self.strategy_updates_per_iteration is not None:
return max(0, int(self.strategy_updates_per_iteration))
return max(0, int(self.strategy_train_steps))
class MemoryConfig(StrictModel):
advantage_capacity: int = 2_000_000
@@ -264,10 +218,8 @@ class MemoryConfig(StrictModel):
class CheckpointConfig(StrictModel):
directory: str = "runs/deep_cfr/default"
save_every: int = 1
save_latest: bool = True
save_every_iteration: bool = True
save_iteration_interval: int = 0
save_latest_only: bool = False
progress_interval_seconds: float = 20.0
exact_resume: bool = False
@@ -277,9 +229,9 @@ class CheckpointConfig(StrictModel):
class EvaluationConfig(StrictModel):
eval_every: int = 0
eval_every: int = 50
games: int = 10
opponents: tuple[str, ...] = ("random",)
opponents: tuple[str, ...] = ("random", "safe_heuristic")
max_steps: int = 10_000
on_max_steps: str = "score_diff"
batch_size: int = 64
@@ -302,9 +254,6 @@ class EvaluationConfig(StrictModel):
raise ValueError("must be 'trainer', 'auto', 'cpu', or 'cuda'")
return token
def resolved_batch_size(self) -> int:
return max(1, int(self.batch_size))
def resolved_num_workers(self, opponent_count: int | None = None) -> int:
workers = max(1, int(self.num_workers))
if opponent_count is not None:
src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py
+17 -23
View File
@@ -329,7 +329,7 @@ class DeepCFRTrainer:
progress_every = int(self.config.traversal.progress_every_traversals)
completed = 0
progress_started = time.perf_counter()
traversals_per_player = self.config.traversal.resolved_traversals_per_player()
traversals_per_player = self.config.traversal.traversals_per_player
for player in range(2):
seeds = [
self.config.run.seed + iteration * 10_000 + traversal_index * 10 + player
@@ -358,7 +358,7 @@ class DeepCFRTrainer:
self.config.traversal.store_strategy_on_opponent_nodes
),
max_depth=self.config.traversal.max_depth,
max_nodes=self.config.traversal.resolved_max_nodes(),
max_nodes=self.config.traversal.max_nodes_per_traversal,
sampling_mode=self.config.traversal.sampling_mode,
outcome_sampling_epsilon=self.config.traversal.outcome_sampling_epsilon,
outcome_sampling_value_clip=self.config.traversal.outcome_sampling_value_clip,
@@ -409,7 +409,7 @@ class DeepCFRTrainer:
self.tracker.log_event(
f"Traversal multiprocessing enabled iteration={iteration} "
f"requested_workers={requested_workers} effective_workers={max_workers} "
f"batches={len(batches)} chunk_size={self.config.traversal.resolved_worker_chunk_size()}"
f"batches={len(batches)} chunk_size={self.config.traversal.worker_chunk_size}"
)
if max_workers < requested_workers:
self.tracker.log_event(
@@ -481,12 +481,12 @@ class DeepCFRTrainer:
name: value.detach().cpu()
for name, value in self.strategy_network.state_dict().items()
}
chunk_size = self.config.traversal.resolved_worker_chunk_size()
chunk_size = self.config.traversal.worker_chunk_size
batch_index = 0
for player in range(2):
seeds = [
self.config.run.seed + iteration * 10_000 + index * 10 + player
for index in range(self.config.traversal.resolved_traversals_per_player())
for index in range(self.config.traversal.traversals_per_player)
]
for start in range(0, len(seeds), chunk_size):
chunk = seeds[start : start + chunk_size]
@@ -574,27 +574,21 @@ class DeepCFRTrainer:
def _stop_iteration(self) -> int:
if self.config.run.max_iterations is not None:
return max(self.iteration, int(self.config.run.max_iterations))
if self.config.run.max_hours is not None:
return 2**31 - 1
if self.config.run.iterations is None:
return 2**31 - 1
return self.iteration + self.config.run.iterations
def _time_limit_reached(self, run_started: float) -> bool:
if self.config.run.max_hours is None:
if self.config.run.max_minutes is None:
return False
elapsed_hours = (time.perf_counter() - run_started) / 3600.0
return elapsed_hours >= self.config.run.max_hours
elapsed_minutes = (time.perf_counter() - run_started) / 60.0
return elapsed_minutes >= self.config.run.max_minutes
def _should_save_iteration(self, iteration: int) -> bool:
if self.config.checkpoint.save_every_iteration:
return True
interval = int(self.config.checkpoint.save_iteration_interval)
interval = int(self.config.checkpoint.save_every)
return interval > 0 and iteration % interval == 0
def _save_iteration_checkpoints(self, iteration: int, item: IterationMetrics) -> None:
checkpoint_dir = self.run_dir
if self._should_save_iteration(iteration) and not self.config.checkpoint.save_latest_only:
if self._should_save_iteration(iteration):
self.save_checkpoint(checkpoint_dir / f"iteration_{iteration:05d}.pt", item)
if self.config.checkpoint.save_latest:
self.save_checkpoint(checkpoint_dir / "latest.pt", item)
@@ -641,7 +635,7 @@ class DeepCFRTrainer:
device=eval_device,
max_steps=self.config.evaluation.max_steps,
encoding=self.config.encoding,
batch_size=self.config.evaluation.resolved_batch_size(),
batch_size=self.config.evaluation.batch_size,
)
for key, value in result.items():
results[f"eval_{opponent}_{key}"] = value
@@ -657,7 +651,7 @@ class DeepCFRTrainer:
self.tracker.log_event(
f"Evaluation multiprocessing enabled iteration={iteration} "
f"effective_workers={max_workers} opponents={len(opponents)} "
f"batch_size={self.config.evaluation.resolved_batch_size()} device={eval_device}"
f"batch_size={self.config.evaluation.batch_size} device={eval_device}"
)
state_dict = self._strategy_state_dict_cpu()
jobs = [
@@ -673,7 +667,7 @@ class DeepCFRTrainer:
seed=self.config.run.seed + iteration * 1000,
device=str(eval_device),
max_steps=self.config.evaluation.max_steps,
batch_size=self.config.evaluation.resolved_batch_size(),
batch_size=self.config.evaluation.batch_size,
)
for opponent in opponents
]
@@ -773,10 +767,10 @@ class DeepCFRTrainer:
) -> float:
losses: list[float] = []
network.train()
for _step in range(self.config.optimization.resolved_advantage_train_steps()):
for _step in range(self.config.optimization.advantage_updates_per_iteration):
sample_started = time.perf_counter()
batch = self.advantage_memories[player].sample(
self.config.optimization.resolved_advantage_batch_size(),
self.config.optimization.advantage_batch_size,
self.rng,
)
self._runtime_metrics[f"advantage_player_{player}_sample_seconds"] = (
@@ -828,10 +822,10 @@ class DeepCFRTrainer:
) -> float:
last_loss = 0.0
network.train()
for _step in range(self.config.optimization.resolved_strategy_train_steps()):
for _step in range(self.config.optimization.strategy_updates_per_iteration):
sample_started = time.perf_counter()
batch = self.strategy_memory.sample(
self.config.optimization.resolved_strategy_batch_size(), self.rng
self.config.optimization.strategy_batch_size, self.rng
)
self._runtime_metrics["strategy_sample_seconds"] = (
float(self._runtime_metrics.get("strategy_sample_seconds", 0.0))
src/coolrl_lost_cities/games/classic/deep_cfr/workers.py
+1 -1
View File
@@ -100,7 +100,7 @@ def run_traversal_worker_batch(batch: TraversalWorkerBatch) -> TraversalWorkerRe
store_strategy_on_traverser_nodes=cfg.traversal.store_strategy_on_traverser_nodes,
store_strategy_on_opponent_nodes=cfg.traversal.store_strategy_on_opponent_nodes,
max_depth=cfg.traversal.max_depth,
max_nodes=cfg.traversal.resolved_max_nodes(),
max_nodes=cfg.traversal.max_nodes_per_traversal,
sampling_mode=cfg.traversal.sampling_mode,
outcome_sampling_epsilon=cfg.traversal.outcome_sampling_epsilon,
outcome_sampling_value_clip=cfg.traversal.outcome_sampling_value_clip,
tests/games/classic/test_deep_cfr_trainer.py
+111 -114
View File
@@ -33,9 +33,9 @@ def _deep_cfr_config(data: dict) -> DeepCFRConfig:
def test_deep_cfr_loads_smoke_yaml_config() -> None:
config = load_config("configs/deep_cfr/smoke.yaml")
assert config.run.iterations == 1
assert config.run.max_iterations == 1
assert config.network.hidden_size == 16
assert config.traversal.traversals_per_iteration == 1
assert config.traversal.traversals_per_player == 1
assert config.checkpoint.directory == "runs/deep_cfr/smoke"
@@ -45,30 +45,30 @@ def test_deep_cfr_loads_mapped_legacy_reproduction_config() -> None:
assert config.run.experiment_name.endswith("slot_playability")
assert config.run.seed == 79
assert config.run.max_iterations is None
assert config.run.max_hours == 4
assert config.run.max_minutes == 240
assert config.encoding.derived_playability is True
assert config.encoding.slot_aware_playability is True
assert config.network.hidden_size == 256
assert config.network.num_layers == 3
assert config.traversal.resolved_traversals_per_player() == 70
assert config.traversal.traversals_per_player == 70
assert config.traversal.sampling_mode == "outcome"
assert config.traversal.max_depth is None
assert config.traversal.resolved_max_nodes() == 1000
assert config.traversal.resolved_worker_chunk_size() == 8
assert config.traversal.max_nodes_per_traversal == 1000
assert config.traversal.worker_chunk_size == 8
assert config.traversal.progress_every_traversals == 10
assert config.optimization.resolved_advantage_batch_size() == 1024
assert config.optimization.resolved_strategy_batch_size() == 1024
assert config.optimization.resolved_advantage_train_steps() == 256
assert config.optimization.resolved_strategy_train_steps() == 256
assert config.optimization.advantage_batch_size == 1024
assert config.optimization.strategy_batch_size == 1024
assert config.optimization.advantage_updates_per_iteration == 256
assert config.optimization.strategy_updates_per_iteration == 256
assert config.optimization.weight_decay == 0.0001
assert config.optimization.grad_clip == 1.0
assert config.evaluation.on_max_steps == "score_diff"
assert config.evaluation.resolved_batch_size() == 64
assert config.evaluation.batch_size == 64
assert config.evaluation.device == "trainer"
assert config.evaluation.resolved_num_workers() == 4
assert config.regret_matching.all_negative_fallback == "uniform"
assert config.training_weighting.mode == "none"
assert config.checkpoint.save_iteration_interval == 10
assert config.checkpoint.save_every == 10
assert (
config.checkpoint.directory == "runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability"
)
@@ -80,17 +80,14 @@ def test_deep_cfr_train_cli_accepts_run_and_traversal_config_overrides() -> None
(),
{
"config_overrides": [
"run.iterations=1",
"run.max_hours=null",
"run.max_iterations=null",
"traversal.traversals_per_iteration=1",
"traversal.traversals_per_player=null",
"run.max_iterations=1",
"run.max_minutes=null",
"traversal.traversals_per_player=1",
"traversal.num_workers=0",
"regret_matching.all_negative_fallback=argmax_tiebreak",
"training_weighting.mode=lcfr",
"checkpoint.save_latest=false",
"checkpoint.save_every_iteration=false",
"checkpoint.save_iteration_interval=0",
"checkpoint.save_every=0",
],
},
)()
@@ -98,15 +95,13 @@ def test_deep_cfr_train_cli_accepts_run_and_traversal_config_overrides() -> None
overridden = _with_overrides(config, _train_overrides_from_args(args))
assert overridden.run.iterations == 1
assert overridden.run.max_hours is None
assert overridden.run.max_iterations is None
assert overridden.traversal.traversals_per_player is None
assert overridden.traversal.resolved_traversals_per_player() == 1
assert overridden.run.max_iterations == 1
assert overridden.run.max_minutes is None
assert overridden.traversal.traversals_per_player == 1
assert overridden.traversal.resolved_num_workers() == 0
assert overridden.regret_matching.all_negative_fallback == "argmax_tiebreak"
assert overridden.training_weighting.mode == "lcfr"
assert overridden.checkpoint.save_every_iteration is False
assert overridden.checkpoint.save_every == 0
assert overridden.checkpoint.save_latest is False
@@ -123,9 +118,7 @@ def test_deep_cfr_train_cli_checkpoint_save_overrides() -> None:
{
"config_overrides": [
"checkpoint.save_latest=true",
"checkpoint.save_latest_only=true",
"checkpoint.save_every_iteration=false",
"checkpoint.save_iteration_interval=1",
"checkpoint.save_every=1",
],
},
)()
@@ -133,9 +126,7 @@ def test_deep_cfr_train_cli_checkpoint_save_overrides() -> None:
overridden = _with_overrides(DeepCFRConfig(), _train_overrides_from_args(args))
assert overridden.checkpoint.save_latest is True
assert overridden.checkpoint.save_latest_only is True
assert overridden.checkpoint.save_every_iteration is False
assert overridden.checkpoint.save_iteration_interval == 1
assert overridden.checkpoint.save_every == 1
def test_deep_cfr_train_cli_accepts_generic_config_overrides() -> None:
@@ -146,8 +137,8 @@ def test_deep_cfr_train_cli_accepts_generic_config_overrides() -> None:
"config_overrides": [
"traversal.sampling_mode=external",
"traversal.max_depth=null",
"optimization.batch_size=64",
"checkpoint.save_latest_only=true",
"optimization.advantage_batch_size=64",
"checkpoint.save_latest=true",
],
},
)()
@@ -156,17 +147,17 @@ def test_deep_cfr_train_cli_accepts_generic_config_overrides() -> None:
assert overridden.traversal.sampling_mode == "external"
assert overridden.traversal.max_depth is None
assert overridden.optimization.batch_size == 64
assert overridden.checkpoint.save_latest_only is True
assert overridden.optimization.advantage_batch_size == 64
assert overridden.checkpoint.save_latest is True
def test_deep_cfr_iteration_weights_use_sample_age() -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 12},
"run": {"max_iterations": 1, "seed": 12},
"network": {"hidden_size": 16},
"checkpoint": {"save_every_iteration": False},
"checkpoint": {"save_every": 0},
"training_weighting": {"mode": "lcfr", "lcfr_alpha": 1.0},
}
),
@@ -279,16 +270,21 @@ def test_deep_cfr_slot_aware_playability_encoding_zero_fills_empty_hand_slots()
def test_deep_cfr_trainer_uses_playability_encoding() -> None:
config = _deep_cfr_config(
{
"run": {"iterations": 1, "seed": 62},
"run": {"max_iterations": 1, "seed": 62},
"encoding": {"derived_playability": True, "slot_aware_playability": True},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_iteration": 1, "max_depth": 1, "max_nodes": 16},
"optimization": {
"advantage_train_steps": 1,
"strategy_train_steps": 1,
"batch_size": 2,
"traversal": {
"traversals_per_player": 1,
"max_depth": 1,
"max_nodes_per_traversal": 16,
},
"checkpoint": {"save_every_iteration": False},
"optimization": {
"advantage_updates_per_iteration": 1,
"strategy_updates_per_iteration": 1,
"advantage_batch_size": 2,
"strategy_batch_size": 2,
},
"checkpoint": {"save_every": 0},
}
)
game_config = LostCitiesConfig(seed=62)
@@ -317,17 +313,17 @@ def test_deep_cfr_trainer_forwards_metrics_to_extra_trackers(tmp_path) -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 71},
"run": {"max_iterations": 1, "seed": 71},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"max_depth": 2,
"max_nodes": 16,
"max_nodes_per_traversal": 16,
},
"optimization": {"batch_size": 2},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "extra-tracker"),
"save_every_iteration": False,
"save_every": 0,
},
}
),
@@ -347,19 +343,20 @@ def test_deep_cfr_trainer_smoke_run() -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 23},
"run": {"max_iterations": 1, "seed": 23},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"max_depth": 3,
"max_nodes": 64,
"max_nodes_per_traversal": 64,
},
"optimization": {
"advantage_train_steps": 1,
"strategy_train_steps": 1,
"batch_size": 2,
"advantage_updates_per_iteration": 1,
"strategy_updates_per_iteration": 1,
"advantage_batch_size": 2,
"strategy_batch_size": 2,
},
"checkpoint": {"save_every_iteration": False},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=23),
@@ -383,20 +380,21 @@ def test_deep_cfr_trainer_supports_lcfr_and_dcfr_loss_weighting() -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 24},
"run": {"max_iterations": 1, "seed": 24},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"max_depth": 2,
"max_nodes": 32,
"max_nodes_per_traversal": 32,
},
"optimization": {
"advantage_train_steps": 1,
"strategy_train_steps": 1,
"batch_size": 2,
"advantage_updates_per_iteration": 1,
"strategy_updates_per_iteration": 1,
"advantage_batch_size": 2,
"strategy_batch_size": 2,
},
"training_weighting": {"mode": mode},
"checkpoint": {"save_every_iteration": False},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=24),
@@ -413,15 +411,15 @@ def test_deep_cfr_cython_traverser_restores_state_and_collects_samples() -> None
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 29},
"run": {"max_iterations": 1, "seed": 29},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"max_depth": 2,
"max_nodes": 32,
"max_nodes_per_traversal": 32,
},
"optimization": {"batch_size": 2},
"checkpoint": {"save_every_iteration": False},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=29),
@@ -457,12 +455,12 @@ def test_deep_cfr_cython_traverser_supports_outcome_sampling_and_rollout_cutoffs
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 31},
"run": {"max_iterations": 1, "seed": 31},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"max_depth": 1,
"max_nodes": 32,
"max_nodes_per_traversal": 32,
"outcome_sampling_epsilon": 0.25,
"outcome_sampling_value_clip": 100.0,
"outcome_unsampled_regret": "zero",
@@ -471,8 +469,8 @@ def test_deep_cfr_cython_traverser_supports_outcome_sampling_and_rollout_cutoffs
"cutoff_rollout_policy": "random",
"cutoff_rollout_max_steps": 16,
},
"optimization": {"batch_size": 2},
"checkpoint": {"save_every_iteration": False},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=31),
@@ -512,16 +510,16 @@ def test_deep_cfr_cython_traverser_supports_external_sampling() -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 33},
"run": {"max_iterations": 1, "seed": 33},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"sampling_mode": "external",
"max_depth": 1,
"max_nodes": 64,
"max_nodes_per_traversal": 64,
},
"optimization": {"batch_size": 2},
"checkpoint": {"save_every_iteration": False},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {"save_every": 0},
}
),
LostCitiesConfig(seed=33),
@@ -563,11 +561,11 @@ def test_deep_cfr_cython_traverser_records_regret_fallback_metrics() -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 37},
"run": {"max_iterations": 1, "seed": 37},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_iteration": 1, "max_depth": 1},
"optimization": {"batch_size": 2},
"checkpoint": {"save_every_iteration": False},
"traversal": {"traversals_per_player": 1, "max_depth": 1},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {"save_every": 0},
"regret_matching": {"all_negative_fallback": "argmax_tiebreak"},
}
),
@@ -637,17 +635,17 @@ def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 41},
"run": {"max_iterations": 1, "seed": 41},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"max_depth": 2,
"max_nodes": 32,
"max_nodes_per_traversal": 32,
},
"optimization": {"batch_size": 2},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(checkpoint_dir),
"save_every_iteration": True,
"save_every": 1,
},
"evaluation": {"eval_every": 1, "games": 2, "opponents": ("random",)},
}
@@ -664,7 +662,7 @@ def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None
"network": {"hidden_size": 16},
"checkpoint": {
"directory": str(checkpoint_dir),
"save_every_iteration": False,
"save_every": 0,
},
}
),
@@ -700,13 +698,12 @@ def test_deep_cfr_trainer_always_saves_latest_checkpoint(tmp_path) -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 42},
"run": {"max_iterations": 1, "seed": 42},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_iteration": 1, "max_depth": 1},
"traversal": {"traversals_per_player": 1, "max_depth": 1},
"checkpoint": {
"directory": str(checkpoint_dir),
"save_every_iteration": False,
"save_iteration_interval": 10,
"save_every": 10,
},
}
),
@@ -724,10 +721,10 @@ def test_deep_cfr_exact_resume_is_explicitly_not_implemented(tmp_path) -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 44},
"run": {"max_iterations": 1, "seed": 44},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_iteration": 1, "max_depth": 1},
"checkpoint": {"directory": str(checkpoint_dir), "save_every_iteration": True},
"traversal": {"traversals_per_player": 1, "max_depth": 1},
"checkpoint": {"directory": str(checkpoint_dir), "save_every": 1},
}
),
LostCitiesConfig(seed=44),
@@ -755,20 +752,20 @@ def test_deep_cfr_trainer_multiprocessing_smoke_run(tmp_path) -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 1, "seed": 43},
"run": {"max_iterations": 1, "seed": 43},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"max_depth": 2,
"max_nodes": 32,
"max_nodes_per_traversal": 32,
"num_workers": 8,
"worker_chunk_size": 1,
"progress_every_traversals": 1,
},
"optimization": {"batch_size": 2},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "mp"),
"save_every_iteration": False,
"save_every": 0,
},
}
),
@@ -791,8 +788,8 @@ def test_deep_cfr_traversal_benchmark_smoke() -> None:
{
"run": {"seed": 47},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_iteration": 1, "max_depth": 2},
"checkpoint": {"save_every_iteration": False},
"traversal": {"traversals_per_player": 1, "max_depth": 2},
"checkpoint": {"save_every": 0},
}
)
)
@@ -804,8 +801,8 @@ def test_deep_cfr_traversal_benchmark_smoke() -> None:
{
"run": {"seed": 48},
"network": {"hidden_size": 16},
"traversal": {"traversals_per_iteration": 1, "max_depth": 2},
"checkpoint": {"save_every_iteration": False},
"traversal": {"traversals_per_player": 1, "max_depth": 2},
"checkpoint": {"save_every": 0},
}
)
)
@@ -816,12 +813,12 @@ def test_deep_cfr_self_play_league_records_snapshots(tmp_path) -> None:
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 2, "seed": 53},
"run": {"max_iterations": 2, "seed": 53},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"max_depth": 2,
"max_nodes": 32,
"max_nodes_per_traversal": 32,
"opponent_policy": "self_play_league",
},
"self_play": {
@@ -829,10 +826,10 @@ def test_deep_cfr_self_play_league_records_snapshots(tmp_path) -> None:
"max_snapshots": 1,
"anchor_probability": 1.0,
},
"optimization": {"batch_size": 2},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "league"),
"save_every_iteration": False,
"save_every": 0,
},
}
),
@@ -849,12 +846,12 @@ def test_deep_cfr_weighted_self_play_league_uses_snapshot_bucket(tmp_path) -> No
trainer = DeepCFRTrainer(
_deep_cfr_config(
{
"run": {"iterations": 2, "seed": 59},
"run": {"max_iterations": 2, "seed": 59},
"network": {"hidden_size": 16},
"traversal": {
"traversals_per_iteration": 1,
"traversals_per_player": 1,
"max_depth": 2,
"max_nodes": 32,
"max_nodes_per_traversal": 32,
"opponent_policy": "self_play_league",
},
"self_play": {
@@ -866,10 +863,10 @@ def test_deep_cfr_weighted_self_play_league_uses_snapshot_bucket(tmp_path) -> No
"anchor_weight": 0.0,
"recent_window": 1,
},
"optimization": {"batch_size": 2},
"optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
"checkpoint": {
"directory": str(tmp_path / "weighted-league"),
"save_every_iteration": False,
"save_every": 0,
},
}
),