id: c950ef4b064544ff9ff6f188ee3ce783
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item_id: 47031623e2c6451fb36730ffe93380ea
item_updated_time: 1781251983046
title_diff: "[]"
body_diff: "[{\"diffs\":[[0,\"map\\\n\"],[-1,\"\\\n## Phase 1: Simulation Testbed ✅\\\n\\\n**Remaining for mass simula\"],[1,\"ID: 47031623e2c6451fb36730ffe93380ea\\\nNotebook ID: 2c8da247905946c3aa19eb4936e16323\\\n\\\n---\\\n\\\n## Phase 1: Simulation Testbed ✅\\\n\\\n**Status: COMPLETE**\\\n\\\n- ✅ Simulation engine (`holdem_core`) — cash games, SNG, MTT\\\n- ✅ Strategy framework — trait-based composition, 14 registered strategies\\\n- ✅ Gen 1 strategies — static rules for preflop and postflop\\\n- ✅ Gen 2 strategies — rollout-based EHS with nut-aware thresholds\\\n- ✅ V3 production baseline — F1 (trap protection) + F4 (OTB draw call)\\\n- ✅ Configurable thresholds — 79 PostflopThresholds fields with TOML overrides\\\n- ✅ A/B testing infrastructure — validated 6 fixes, promoted 2 to produc\"],[0,\"tion\"],[-1,\"**\"],[0,\"\\\n- \"],[-1,\"GUI adaption for configuration of simulations and bots\"],[1,\"✅ Parameter sweep framework — 19 params × 2 directions × 2 scenarios = 76 sims completed\\\n- ✅ **Experiment GUI** (`holdem_gui`) — web-based parameter sweep tool with:\\\n  - Bot config editor (load/edit/save TOML)\\\n  - Experiment builder (parameter selection, range input, table composition)\\\n  - Two sweep modes: VsBaseline and RoundRobin\\\n  - Live progress tracking, results table with Chart.js visualization\\\n  - REST API on 127.0.0.1:3000, spawns simulations as subprocesses\\\n\\\n---\\\n\\\n## Phase 2: Optimization & Tuning 🔄\\\n\\\n**Current focus: refining V3 thresholds using the experiment GUI**\\\n\\\n- [ ] Apply top 5 parameter sweep findings (nut_draw_ppot_thresh, call_turn_small_bet, rope_a_dope_otb, raise_default, call_otb_turn_small_bet)\\\n- [ ] Validate combined V3.1 config across all 4 scenarios (field, flock, HU, 5v5)\\\n- [ ] Explore wider ranges (±5% steps) for the most promising parameters\\\n- [ ] Multi-parameter sweeps (combinatorial exploration of correlated thresholds)\\\n- [ ] SNG-specific parameter tuning (buy_in, blind levels, prize structures)\"],[0,\"\\\n\\\n--\"]],\"start1\":30,\"start2\":30,\"length1\":133,\"length2\":1687},{\"diffs\":[[0,\"les \"],[-1,\"(modular strategy factory)\"],[1,\"✅\"],[0,\"\\\n- N\"]],\"start1\":1945,\"start2\":1945,\"length1\":34,\"length2\":9},{\"diffs\":[[0,\"endgame \"],[-1,\"– port from Java\"],[1,\"✅ (g2_nash_icm)\"],[0,\"\\\n- Stati\"]],\"start1\":1989,\"start2\":1989,\"length1\":32,\"length2\":31},{\"diffs\":[[0,\"ng (\"],[-1,\"possibly with GPU integration\"],[1,\"Gen 3\"],[0,\")\\\n- \"]],\"start1\":2124,\"start2\":2124,\"length1\":37,\"length2\":13},{\"diffs\":[[0,\"skeleton\"],[1,\" (Gen 4)\"],[0,\"\\\n- Hybri\"]],\"start1\":2220,\"start2\":2220,\"length1\":16,\"length2\":24},{\"diffs\":[[0,\"iles)\\\n\\\n---\\\n\\\n\"],[1,\"### Gen 3: Opponent Modelling (Future)\\\n\\\n**Goal:** Weighted range estimation from observed actions\\\n\\\n- Opponent action history tracking\\\n- Range narrowing based on bet sizing patterns\\\n- Position-aware opponent modeling\\\n- Adaptive threshold adjustment based on opponent types\\\n- Integration with existing rollout-based EHS framework\\\n\\\n### Gen 4: Reinforcement Learning (Future)\\\n\\\n**Goal:** Self-play optimization\\\n\\\n- Training environment using `holdem_core` simulation\\\n- State representation (hand strength, pot odds, position, opponent actions)\\\n- Action space (fold, call, raise sizes)\\\n- Reward function (profit-based with variance reduction)\\\n- Policy network training loop\\\n\\\n---\\\n\\\n\"],[0,\"### Live Ser\"]],\"start1\":2338,\"start2\":2338,\"length1\":24,\"length2\":697}]"
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updated_time: 2026-06-12T08:16:35.143Z
created_time: 2026-06-12T08:16:35.143Z
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