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item_updated_time: 1782534454217
title_diff: "[]"
body_diff: "[{\"diffs\":[[0,\"026-06-2\"],[-1,\"6\"],[1,\"7\"],[0,\"\\\n\\\n## Sta\"]],\"start1\":35,\"start2\":35,\"length1\":17,\"length2\":17},{\"diffs\":[[0,\"tatus: F\"],[1,\"ull f\"],[0,\"ramework\"]],\"start1\":50,\"start2\":50,\"length1\":16,\"length2\":21},{\"diffs\":[[0,\"ork \"],[-1,\"skeleton complete + feature extraction working\"],[1,\"+ neural network + training pipeline working\\\n\\\n## Decision: candle-rs (pure Rust ML)\\\n\\\nChosen by user. No Python dependency. Train and inference all in Rust.\"],[0,\"\\\n\\\n##\"]],\"start1\":68,\"start2\":68,\"length1\":54,\"length2\":163},{\"diffs\":[[0,\" masking\"],[1,\" (7 tests)\"],[0,\"\\\n- `feat\"]],\"start1\":399,\"start2\":399,\"length1\":16,\"length2\":26},{\"diffs\":[[0,\"on (\"],[-1,\"card/equity + hand category + draws + board texture + betting/situation + hero state\"],[1,\"5 tests\"],[0,\")\\\n- \"]],\"start1\":467,\"start2\":467,\"length1\":92,\"length2\":15},{\"diffs\":[[0,\"der \"],[-1,\"for offline training\"],[1,\"(3 tests)\"],[0,\"\\\n- `\"]],\"start1\":520,\"start2\":520,\"length1\":28,\"length2\":17},{\"diffs\":[[0,\"mpl \"],[-1,\"(teacher delegation + recording)\\\n\\\n### Registered as `gen5_rl` (strategy #20)\\\n\\\n### Action Space (`actions.rs`)\\\n11 discrete actions:\\\n```\\\n0: Fold       3: Bet 1/3    6: Bet pot    8: Raise 2.5x\\\n1: Check      4: Bet 1/2    7: Bet 2x     9: Raise pot\\\n2: Call       5: Bet 2/3                  10: All-in\\\n```\\\n- `legal_action_mask()` — returns `[bool; 11]` based on to_call, pot, stack\\\n- `action_to_engine()` — maps DiscreteAction → engine `Action`, clamps to stack\\\n- 7 unit tests\\\n\\\n### Feature Vector (`features.rs`, 280 dims\"],[1,\"with model inference (3 tests)\\\n- `network.rs` — **candle-nn MLP + Trainer** behind `gen5_nn` feature (4 tests)\\\n\\\n### Neural Network (`network.rs`)\\\n```\\\nInput(280) → Linear(512) → ReLU\\\n           → Linear(512) → ReLU\\\n           → Linear(256) → ReLU\\\n           → Actor head: Linear(11)  [with legal action masking]\\\n           → Value head: Linear(1)\\\n```\\\n- `PokerPolicy::predict(features, legal_mask)` → `(action, probs, value)` for inference\\\n- `PokerPolicy::forward(features, legal_mask)` → `(logits, value)` for batch training\\\n- `Trainer::new(lr, device)` — creates optimizer + model\\\n- `Trainer::train_batch(samples)` — cross-entropy loss + AdamW backward step\\\n- `Trainer::evaluate(samples)` — accuracy on validation set\\\n- `Trainer::save(path)` — safetensors format\\\n- `save_model()` / `load_model()` — standalone save/load\\\n\\\n### RL Strategy Model Integration\\\n- `model_infer()` uses `PokerPolicy::predict()` when `gen5_nn` feature is enabled\\\n- Falls back to teacher strategy when model not loaded or `use_teacher=true`\\\n- Graceful error handling: inference errors fall back to teacher\\\n\\\n### Feature Vector (280 dims, 215 active\"],[0,\")\\\n| \"]],\"start1\":573,\"start2\":573,\"length1\":526,\"length2\":1128},{\"diffs\":[[0,\"us |\"],[-1,\" Source |\"],[0,\"\\\n|--\"]],\"start1\":1722,\"start2\":1722,\"length1\":17,\"length2\":8},{\"diffs\":[[0,\"-------|\"],[-1,\"--------|\"],[0,\"\\\n| Card/\"]],\"start1\":1746,\"start2\":1746,\"length1\":25,\"length2\":16},{\"diffs\":[[0,\"| ✅ \"],[-1,\"DONE | PotentialResult (\"],[0,\"hs, \"]],\"start1\":1773,\"start2\":1773,\"length1\":32,\"length2\":8},{\"diffs\":[[0,\"ot, rpot\"],[-1,\")\"],[0,\" |\\\n| Han\"]],\"start1\":1797,\"start2\":1797,\"length1\":17,\"length2\":16},{\"diffs\":[[0,\" | ✅\"],[-1,\" DONE | HandHelper::evaluate_hand →\"],[0,\" one\"]],\"start1\":1827,\"start2\":1827,\"length1\":43,\"length2\":8},{\"diffs\":[[0,\"ot (\"],[-1,\"0=\"],[0,\"HighCard\"],[-1,\"…8=\"],[1,\"→\"],[0,\"Stra\"]],\"start1\":1837,\"start2\":1837,\"length1\":21,\"length2\":17},{\"diffs\":[[0,\"lush\"],[-1,\"/RoyalFlush\"],[0,\") |\\\n\"]],\"start1\":1859,\"start2\":1859,\"length1\":19,\"length2\":8},{\"diffs\":[[0,\" | 6 | ✅\"],[-1,\" DONE |\"],[0,\" flush_d\"]],\"start1\":1879,\"start2\":1879,\"length1\":23,\"length2\":16},{\"diffs\":[[0,\", paired\"],[-1,\"_board\"],[0,\", monoto\"]],\"start1\":1913,\"start2\":1913,\"length1\":22,\"length2\":16},{\"diffs\":[[0,\"monotone\"],[-1,\"_board\"],[0,\", connec\"]],\"start1\":1923,\"start2\":1923,\"length1\":22,\"length2\":16},{\"diffs\":[[0,\"cted\"],[-1,\"_board\"],[0,\" |\\\n|\"]],\"start1\":1938,\"start2\":1938,\"length1\":14,\"length2\":8},{\"diffs\":[[0,\"| 10 | ✅\"],[-1,\" DONE |\"],[0,\" board_c\"]],\"start1\":1961,\"start2\":1961,\"length1\":23,\"length2\":16},{\"diffs\":[[0,\"ard \"],[-1,\"+ 5 padding \"],[0,\"|\\\n| \"]],\"start1\":2023,\"start2\":2023,\"length1\":20,\"length2\":8},{\"diffs\":[[0,\"| 25 | ✅\"],[-1,\" DONE |\"],[0,\" pot_bb,\"]],\"start1\":2049,\"start2\":2049,\"length1\":23,\"length2\":16},{\"diffs\":[[0,\"tion\"],[-1,\"(9)\"],[0,\", street\"],[-1,\"(4)\"],[0,\", counts\"],[-1,\"(3)\"],[0,\" |\\\n|\"]],\"start1\":2097,\"start2\":2097,\"length1\":33,\"length2\":24},{\"diffs\":[[0,\" | ✅\"],[-1,\" DONE |\"],[0,\" wag\"]],\"start1\":2136,\"start2\":2136,\"length1\":15,\"length2\":8},{\"diffs\":[[0,\"ING \"],[-1,\"| 3 × 20 (vpip, pfr, af, frequencies, range, stack_bb\"],[1,\"(need Observer access\"],[0,\") |\\\n\"]],\"start1\":2214,\"start2\":2214,\"length1\":61,\"length2\":29},{\"diffs\":[[0,\"0 | \"],[-1,\"⬜ PENDING | 8 × 20\"],[1,\"✅ last 8 actions\"],[0,\" (pl\"]],\"start1\":2264,\"start2\":2264,\"length1\":26,\"length2\":24},{\"diffs\":[[0,\"tion\"],[-1,\" \"],[1,\"_\"],[0,\"type\"],[-1,\" one-hot\"],[0,\", am\"]],\"start1\":2304,\"start2\":2304,\"length1\":21,\"length2\":13},{\"diffs\":[[0,\" |\\\n\\\n\"],[-1,\"**Active features: 55/280** (card/equity + hand category + draws + board texture + betting + hero)\\\n\\\n### Transition Recorder (`recorder.rs`)\\\n- JSONL format: one `Transition` per line\\\n- Fields: hand_id, decision_idx, features[], legal_mask[], action, is_teacher, reward, hand_reward, is_terminal, street, position\\\n- Thread-safe via `Mutex<BufWriter>`\\\n- Flushes every 1000 transitions + on Drop\\\n- 3 unit tests\"],[1,\"### Data Collection & Training\\\n- `configs/bots/gen5_collect.toml` — bot config for imitation learning data collection\\\n- `scripts/train_gen5.py` — Python training script (backup, candle is primary)\\\n- Rust Trainer ready for JSONL → train → safetensors pipeline\"],[0,\"\\\n\\\n\"],[-1,\"#\"],[0,\"## \"],[-1,\"RL Strategy (`rl_strategy.rs`)\\\n- Implements `Strategy` trait (`&self` only — uses `Mutex<RlState>`)\\\n- **Mode 1 (current):** Records transitions while delegating to Gen 3 fallbac\"],[1,\"Tests\\\n- Without candle: 454 tests pass\\\n- With candle (--features gen5_nn): 458 tests pass (454 + 4 networ\"],[0,\"k \"],[-1,\"(\"],[0,\"te\"],[-1,\"acher)\\\n- **Mode 2 (TODO):** Loads model weights, runs forward pass for inference\\\n- Config from TOML: `model_path`, `transitions_path`, `record_transitions`, `use_teacher`, `epsilon`\\\n- 3 unit tests\\\n\\\n### Python Training Script (`scripts/train_gen5.py`)\\\n- **PokerPolicy**: MLP (280→512→512→256→Actor(11)+Value(1)) with legal action masking\\\n- **TransitionDataset**: loads JSONL → tensors\\\n- **train()**: cross-entropy loss, Adam optimizer, cosine LR, train/val split\\\n- **analyze_actions()**: action distribution analysis\\\n- PyTorch NOT installed yet (`pip install torch`)\\\n\\\n### Data Collection Config (`configs/bots/gen5_collect.toml`)\\\n- Bot config that us\"],[1,\"sts)\\\n- Release binary built with `--features gen5_nn`\\\n\\\n## Build Commands\\\n```bash\\\n# Normal build (no neural network):\\\ncargo build --release -p holdem_bots\\\n\\\n# With neural network support:\\\ncargo build --release -p holdem_bots --featur\"],[0,\"es \"],[-1,\"`\"],[0,\"gen5_\"],[-1,\"rl` in teacher mode with transition recording\\\n- Outputs to `/tmp/gen5_transitions.jsonl`\\\n\\\n## Tests\\\n- 19 new Gen 5 unit tests, all pass\\\n- Full suite: 454 lib tests pass (+ 11 doc tests)\\\n\\\n## What's NOT Implemented Yet\\\n\\\n### 1. Opponent Model Features (60 dims)\\\nNeed to wire Gen 4 Observer data → `OpponentFeatures`. The Observer tracks per-player stats (vpip, pfr, af, etc.) that need to be extracted and converted to f32 features.\\\n\\\n**Blocker:** Need to understand how to access the Observer from within the RL strategy. The Observer is typically shared via `Arc<Mutex<Observer>>` in the Gen 4 adaptive strategy. The RL strategy would need a reference to the same Observer.\\\n\\\n### 2. Action History Features (160 dims)\"],[1,\"nn\\\n\\\n# Run tests with neural network:\\\ncargo test -p holdem_bots --features gen5_nn\\\n```\\\n\\\n## What's NOT Implemented Yet\\\n\\\n### 1. Opponent Model Features (60 dims)\\\nNeed Observer access in RL strategy. Open question for user.\\\n\\\n### 2. JSONL Training Driver\"],[0,\"\\\nNeed \"],[-1,\"to convert `ActionRecorder` data → `ActionFeatures`. The ActionRecorder tracks all actions in the current hand.\\\n\\\n**Blocker:** Need to iterate ActionRecorder entries and convert each to the 5-field tuple format.\\\n\\\n### 3. Neural Network Forward Pass\\\nThe `model_infer()` method is a stub. Need to:\\\n1. Choose ML framework: candle-rs (Rust-native) vs ONNX runtime vs tch-rs\\\n2. Implement MLP forward pass in Rust for inference\\\n3. Load PyTorch-trained weights into Rust model\\\n\\\n### 4. Training Pipeline\\\n1. Collect data: `gen5_collect.toml` bot config → JSONL transitions\\\n2. Train: `python scripts/train_gen5.py --data ...`\\\n3. Export: PyTorch → ONNX (for Rust inference)\\\n4. Deploy: Load ONNX in Rust, replace teacher with model\\\n\\\n## Open Questions for User\\\n\\\n1. **ML Framework for inference:** candle-rs (pure Rust, no deps) vs ONNX runtime (mature, GPU) vs tch-rs (PyTorch bindings)?\\\n   - **My recommendation:** ONNX runtime —\"],[1,\"a command-line tool or script that:\\\n1. Reads JSONL transition file\\\n2. Batches samples\\\n3. Runs Trainer::train_batch in a loop\\\n4. Evaluates on validation set\\\n5. Saves best model\\\n\\\nThe Trainer is ready, just needs the driver loop.\\\n\\\n### 3. Self-play Data Generation\\\nNeed to run simulations with `gen5_collect.toml` to generate\"],[0,\" train\"],[-1,\" \"],[0,\"in\"],[-1,\" PyTorch, export to ONNX, load in Rust. Clean separation.\\\n   - **Alternative:** candle-rs — single binary, no external runtime, but smaller community.\\\n\\\n2\"],[1,\"g data.\\\n\\\n## Remaining Open Questions for User\\\n\\\n1\"],[0,\". **\"]],\"start1\":2323,\"start2\":2323,\"length1\":3056,\"length2\":1251},{\"diffs\":[[0,\"r access\"],[1,\" pattern\"],[0,\":** How \"]],\"start1\":3581,\"start2\":3581,\"length1\":16,\"length2\":24},{\"diffs\":[[0,\" should \"],[-1,\"the \"],[0,\"RL strat\"]],\"start1\":3604,\"start2\":3604,\"length1\":20,\"length2\":16},{\"diffs\":[[0,\"Observer\"],[-1,\" data\"],[0,\"?\\\n   - O\"]],\"start1\":3637,\"start2\":3637,\"length1\":21,\"length2\":16},{\"diffs\":[[0,\">>` \"],[-1,\"as \"],[1,\"vi\"],[0,\"a co\"]],\"start1\":3686,\"start2\":3686,\"length1\":11,\"length2\":10},{\"diffs\":[[0,\"eter\"],[-1,\" to the RL strategy\\\n   - Option B: Store Observer in a global/thread-local and access via strategy\\\n   - Option C: The\"],[1,\"\\\n   - Option B:\"],[0,\" RL \"]],\"start1\":3706,\"start2\":3706,\"length1\":125,\"length2\":23},{\"diffs\":[[0,\"rver\"],[-1,\" (duplicates tracking)\"],[0,\"\\\n   - **\"],[-1,\"My r\"],[1,\"R\"],[0,\"ecom\"]],\"start1\":3758,\"start2\":3758,\"length1\":42,\"length2\":17},{\"diffs\":[[0,\"on A\"],[-1,\" (explicit dependency injection)\"],[0,\"\\\n\\\n\"],[-1,\"3\"],[1,\"2\"],[0,\". **\"]],\"start1\":3792,\"start2\":3792,\"length1\":43,\"length2\":11},{\"diffs\":[[0,\":** \"],[-1,\"Start with HU (5× faster convergence) or 6-max (target format)\"],[1,\"HU first (5× faster) or 6-max\"],[0,\"?\\\n  \"]],\"start1\":3820,\"start2\":3820,\"length1\":70,\"length2\":37},{\"diffs\":[[0,\"r 6-max?\\\n   - **\"],[-1,\"My r\"],[1,\"R\"],[0,\"ecommendation:**\"]],\"start1\":3846,\"start2\":3846,\"length1\":36,\"length2\":33},{\"diffs\":[[0,\":** \"],[-1,\"Start HU, expand to 6-max after v1 converges\\\n\\\n4\"],[1,\"HU first\\\n\\\n3\"],[0,\". **\"]],\"start1\":3876,\"start2\":3876,\"length1\":55,\"length2\":19},{\"diffs\":[[0,\":** Per-hand BB \"],[-1,\"delta \"],[0,\"with all-in equi\"]],\"start1\":3908,\"start2\":3908,\"length1\":38,\"length2\":32},{\"diffs\":[[0,\"ment\"],[-1,\", or per-street reward shaping\"],[0,\"?\\\n  \"]],\"start1\":3949,\"start2\":3949,\"length1\":38,\"length2\":8},{\"diffs\":[[0,\"ustment?\\\n   - **\"],[-1,\"My r\"],[1,\"R\"],[0,\"ecommendation:**\"]],\"start1\":3946,\"start2\":3946,\"length1\":36,\"length2\":33},{\"diffs\":[[0,\":** \"],[-1,\"Per-hand BB delta with all-in equity adjustment (cleaner, less noise)\\\n\\\n5\"],[1,\"Yes\\\n\\\n4\"],[0,\". **\"]],\"start1\":3976,\"start2\":3976,\"length1\":80,\"length2\":14},{\"diffs\":[[0,\"lay \"],[-1,\"opponent \"],[0,\"pool\"]],\"start1\":3996,\"start2\":3996,\"length1\":17,\"length2\":8},{\"diffs\":[[0,\" Mix\"],[-1,\" of \"],[1,\"ed (\"],[0,\"Gen 1-4 \"],[-1,\"bots + earlier checkpoints of the policy,\"],[1,\"+ checkpoints)\"],[0,\" or \"]],\"start1\":4007,\"start2\":4007,\"length1\":61,\"length2\":34},{\"diffs\":[[0,\"- **\"],[-1,\"My r\"],[1,\"R\"],[0,\"ecom\"]],\"start1\":4060,\"start2\":4060,\"length1\":12,\"length2\":9},{\"diffs\":[[0,\"pool\"],[-1,\" (70% similar-strength, 20% weaker, 10% stronger)\\\n\\\n## Implementation Steps (clear, no questions needed)\\\n1. ✅ Feature extraction: card/equity, hand category, draws, board texture\\\n2. ⬜ Wire Gen 4 Observer → OpponentFeatures (after Q2 answered)\\\n3. ⬜ Wire ActionRecorder → ActionFeatures\\\n4. ⬜ Collect imitation learning data (run gen5_collect config)\\\n5. ⬜ Install PyTorch, train initial model\\\n6. ⬜ Implement ONNX inference in Rust (after Q1 answered)\"]],\"start1\":4088,\"start2\":4088,\"length1\":450,\"length2\":4}]"
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updated_time: 2026-06-27T04:27:36.396Z
created_time: 2026-06-27T04:27:36.396Z
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