Add heuristic_balanced opponent_policy to interleaved scheduler
Mirrors the discard_only plumbing pattern. Uses HeuristicBot() (default balanced params) and converts the bot's phase-local action to unified via state.to_unified_action. Recursive (Cython) path was already supported and is unchanged. Tests: smoke run + accept/reject validators. All 59 tests pass.
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@@ -139,7 +139,10 @@ def test_deep_cfr_config_accepts_interleaved_scheduler() -> None:
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def test_deep_cfr_config_rejects_unsupported_interleaved_options() -> None:
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with pytest.raises(
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ValueError, match="opponent_policy='network', 'average_strategy', or 'discard_only'"
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ValueError,
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match=(
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"opponent_policy='network', 'average_strategy', 'discard_only', or 'heuristic_balanced'"
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),
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):
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_deep_cfr_config(
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{"traversal": {"scheduler": "interleaved", "opponent_policy": "self_play_league"}}
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@@ -163,6 +166,13 @@ def test_deep_cfr_config_accepts_discard_only_with_interleaved() -> None:
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assert config.traversal.opponent_policy == "discard_only"
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def test_deep_cfr_config_accepts_heuristic_balanced_with_interleaved() -> None:
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config = _deep_cfr_config(
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{"traversal": {"scheduler": "interleaved", "opponent_policy": "heuristic_balanced"}}
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)
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assert config.traversal.opponent_policy == "heuristic_balanced"
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def test_deep_cfr_config_rejects_discard_only_with_recursive() -> None:
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with pytest.raises(ValueError, match="discard_only.*scheduler='interleaved'"):
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_deep_cfr_config(
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@@ -504,6 +514,42 @@ def test_deep_cfr_trainer_discard_only_opponent_smoke_run(tmp_path) -> None:
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assert metrics[0].traversal_nodes > 0
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def test_deep_cfr_trainer_heuristic_balanced_opponent_smoke_run(tmp_path) -> None:
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trainer = DeepCFRTrainer(
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_deep_cfr_config(
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{
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"run": {"max_iterations": 1, "seed": 27},
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"network": {"hidden_size": 16},
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"traversal": {
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"scheduler": "interleaved",
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"opponent_policy": "heuristic_balanced",
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"traversals_per_player": 2,
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"max_depth": 3,
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"max_nodes_per_traversal": 64,
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"interleave_width": 4,
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"interleave_max_batch": 8,
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},
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"optimization": {
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"advantage_updates_per_iteration": 1,
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"strategy_updates_per_iteration": 1,
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"advantage_batch_size": 2,
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"strategy_batch_size": 2,
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},
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"checkpoint": {"save_every": 0, "save_latest": False},
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"evaluation": {"eval_every": 0},
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}
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),
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LostCitiesConfig(seed=27),
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run_dir=tmp_path / "heuristic_balanced",
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)
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metrics = trainer.train()
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assert len(metrics) == 1
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assert metrics[0].advantage_samples > 0
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assert metrics[0].traversal_nodes > 0
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def test_deep_cfr_interleaved_scheduler_matches_recursive_single_traversal() -> None:
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config = _deep_cfr_config(
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{
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