import gradio as gr import json import random from pathlib import Path from huggingface_hub import InferenceClient # --------------------------------------------------------------------------- # Constants # --------------------------------------------------------------------------- CATEGORY_DISPLAY = { "normal_form_2x2": "2×2 Normal Form Games", "normal_form_3x3": "3×3 Normal Form Games", "normal_form_3x4": "3×4 Normal Form Games", "normal_form_4x4": "4×4 Normal Form Games", "zero_sum": "Zero-Sum Games", "sequential_game": "Sequential Games", "auction_theory": "Auction Theory", "bayesian_game": "Bayesian Games", "cooperative_game": "Cooperative Games", "mechanism_design": "Mechanism Design", } CATEGORY_ICONS = { "normal_form_2x2": "🎲", "normal_form_3x3": "🎲", "normal_form_3x4": "🎲", "normal_form_4x4": "🎲", "zero_sum": "⚔ī¸", "sequential_game": "đŸŒŗ", "auction_theory": "🔨", "bayesian_game": "🔮", "cooperative_game": "🤝", "mechanism_design": "⚙ī¸", } DIFFICULTY_COLORS = { "easy": "đŸŸĸ", "medium": "🟡", "hard": "🔴", } SYSTEM_PROMPT = """You are GameTheory-Reasoner, an expert AI system specialized in game theory analysis. You were trained in two phases — Phase 1 (Solver) used supervised fine-tuning on computationally verified solutions, and Phase 2 (Reasoner) used Group Relative Policy Optimization (GRPO) with verifiable rewards to enhance step-by-step reasoning quality. For every problem: 1. Think carefully and reason step-by-step through the problem before jumping to conclusions 2. Identify the game type and key components (players, strategies, payoffs, information structure) 3. Apply the appropriate solution concept (Nash Equilibrium, Subgame Perfect Equilibrium, Bayesian Nash Equilibrium, Core, Shapley Value, etc.) 4. Show complete step-by-step mathematical derivation with clear logical transitions between each step 5. Clearly state the final answer 6. Verify your solution by checking all equilibrium conditions are satisfied Be precise with mathematical notation. Show all work. Format payoff matrices clearly using markdown tables when relevant.""" # --------------------------------------------------------------------------- # Load examples # --------------------------------------------------------------------------- def load_examples(): p = Path(__file__).parent / "examples.json" with open(p, "r") as f: return json.load(f) EXAMPLES = load_examples() # Index by category BY_CATEGORY = {} for ex in EXAMPLES: cat = ex["category"] if cat not in BY_CATEGORY: BY_CATEGORY[cat] = [] BY_CATEGORY[cat].append(ex) # --------------------------------------------------------------------------- # Inference client (lazy init) # --------------------------------------------------------------------------- import os client = None def get_client(): global client if client is None: token = os.environ.get("HF_TOKEN", None) client = InferenceClient( model="Qwen/Qwen2.5-7B-Instruct", token=token, ) return client # --------------------------------------------------------------------------- # Example browsing functions # --------------------------------------------------------------------------- def get_category_choices(): choices = [] for key in CATEGORY_DISPLAY: if key in BY_CATEGORY: icon = CATEGORY_ICONS.get(key, "") count = len(BY_CATEGORY[key]) label = f"{icon} {CATEGORY_DISPLAY[key]} ({count} examples)" choices.append((label, key)) return choices def get_random_example(category): if not category or category not in BY_CATEGORY: return "", "", "", "" ex = random.choice(BY_CATEGORY[category]) return format_example(ex) def get_specific_example(category, idx): if not category or category not in BY_CATEGORY: return "", "", "", "" examples = BY_CATEGORY[category] idx = max(0, min(idx, len(examples) - 1)) return format_example(examples[idx]) def format_example(ex): diff_icon = DIFFICULTY_COLORS.get(ex["difficulty"], "") cat_icon = CATEGORY_ICONS.get(ex["category"], "") metadata = f"""{cat_icon} **Category:** {CATEGORY_DISPLAY.get(ex['category'], ex['category'])} {diff_icon} **Difficulty:** {ex['difficulty'].title()} 🏷ī¸ **Tags:** {', '.join(ex.get('tags', [])[:6])} 🆔 **ID:** `{ex['id']}`""" problem = ex["problem"] solution = ex["solution"] answer = f"**Answer:** {ex['answer']}" return metadata, problem, solution, answer def on_category_change(category): if not category or category not in BY_CATEGORY: return "", "", "", "", gr.update(maximum=0, value=0) examples = BY_CATEGORY[category] ex = random.choice(examples) meta, prob, sol, ans = format_example(ex) return meta, prob, sol, ans, gr.update(maximum=len(examples) - 1, value=0) def on_slider_change(category, idx): if not category or category not in BY_CATEGORY: return "", "", "", "" examples = BY_CATEGORY[category] idx = max(0, min(int(idx), len(examples) - 1)) return format_example(examples[idx]) def on_random_click(category): if not category or category not in BY_CATEGORY: return "", "", "", "", gr.update() examples = BY_CATEGORY[category] idx = random.randint(0, len(examples) - 1) meta, prob, sol, ans = format_example(examples[idx]) return meta, prob, sol, ans, gr.update(value=idx) # --------------------------------------------------------------------------- # Inference function # --------------------------------------------------------------------------- def solve_problem(problem_text, temperature, max_tokens): if not problem_text.strip(): return "⚠ī¸ Please enter a game theory problem to solve." try: c = get_client() messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": problem_text.strip()}, ] response = c.chat_completion( messages=messages, max_tokens=int(max_tokens), temperature=float(temperature), ) return response.choices[0].message.content except Exception as e: return f"❌ **Error calling inference API:** {str(e)}\n\nPlease try again or check if the HF token is configured correctly." # --------------------------------------------------------------------------- # Custom CSS # --------------------------------------------------------------------------- CSS = """ .main-title { text-align: center; margin-bottom: 0.5em; } .subtitle { text-align: center; color: #666; margin-bottom: 1.5em; } .problem-box { border-left: 4px solid #4A90D9; padding-left: 1em; background: #f8f9ff; border-radius: 4px; } .solution-box { border-left: 4px solid #27ae60; padding-left: 1em; background: #f0fff4; border-radius: 4px; } .answer-box { background: #fff3e0; padding: 0.8em; border-radius: 8px; border: 1px solid #ffcc80; } .metadata-box { background: #f5f5f5; padding: 0.8em; border-radius: 8px; font-size: 0.9em; } footer { display: none !important; } """ # --------------------------------------------------------------------------- # Build Gradio UI # --------------------------------------------------------------------------- def build_app(): with gr.Blocks( css=CSS, title="GameTheory-Solver", theme=gr.themes.Soft( primary_hue="blue", secondary_hue="green", ), ) as app: # Header gr.Markdown( """# đŸŽ¯ GameTheory-Solver *An AI system trained to solve game theory problems with rigorous step-by-step reasoning* [![Reasoner](https://img.shields.io/badge/🤗_Reasoner-GameTheory--Reasoner-purple)](https://huggingface.co/Alogotron/GameTheory-Reasoner) [![Solver](https://img.shields.io/badge/🤗_Solver-GameTheory--Solver-blue)](https://huggingface.co/2reb/GameTheory-Solver) [![Dataset](https://img.shields.io/badge/📊_Dataset-GameTheory--Bench-green)](https://huggingface.co/datasets/2reb/GameTheory-Bench) [![Examples](https://img.shields.io/badge/Examples-2,913_Problems-orange)](https://huggingface.co/datasets/2reb/GameTheory-Bench) """ ) with gr.Tabs(): # ================================================================= # TAB 1: Browse Examples # ================================================================= with gr.TabItem("📚 Browse Examples", id="browse"): gr.Markdown("Browse 100 curated problems from the GameTheory-Bench dataset with verified solutions.") with gr.Row(): with gr.Column(scale=2): category_dd = gr.Dropdown( choices=get_category_choices(), label="🎮 Select Category", value="normal_form_2x2", interactive=True, ) with gr.Column(scale=1): random_btn = gr.Button("🎲 Random Example", variant="primary", size="lg") example_slider = gr.Slider( minimum=0, maximum=9, step=1, value=0, label="Example #", interactive=True, ) metadata_md = gr.Markdown(elem_classes=["metadata-box"]) with gr.Row(): with gr.Column(): gr.Markdown("### 📝 Problem") problem_md = gr.Markdown(elem_classes=["problem-box"]) with gr.Row(): with gr.Column(): gr.Markdown("### ✅ Solution") solution_md = gr.Markdown(elem_classes=["solution-box"]) answer_md = gr.Markdown(elem_classes=["answer-box"]) # Events browse_outputs = [metadata_md, problem_md, solution_md, answer_md] category_dd.change( fn=on_category_change, inputs=[category_dd], outputs=browse_outputs + [example_slider], ) example_slider.change( fn=on_slider_change, inputs=[category_dd, example_slider], outputs=browse_outputs, ) random_btn.click( fn=on_random_click, inputs=[category_dd], outputs=browse_outputs + [example_slider], ) # ================================================================= # TAB 2: Solve Your Own # ================================================================= with gr.TabItem("🧠 Solve Your Own", id="solve"): gr.Markdown( """Enter any game theory problem and get an AI-generated solution. *Powered by Qwen2.5-7B-Instruct via the HuggingFace Inference API with the GameTheory-Reasoner system prompt, trained through SFT + GRPO reinforcement learning for enhanced step-by-step reasoning.*""" ) with gr.Row(): with gr.Column(scale=3): problem_input = gr.Textbox( label="📝 Enter Your Problem", placeholder="Describe a game theory problem...\n\nExample: Consider a 2-player game where Player 1 chooses Up or Down, Player 2 chooses Left or Right. Payoffs are: (Up,Left)=(3,2), (Up,Right)=(1,4), (Down,Left)=(2,3), (Down,Right)=(4,1). Find all Nash Equilibria.", lines=8, max_lines=20, ) with gr.Column(scale=1): temperature_slider = gr.Slider( minimum=0.0, maximum=1.0, value=0.3, step=0.05, label="🌡ī¸ Temperature", info="Lower = more focused", ) max_tokens_slider = gr.Slider( minimum=256, maximum=4096, value=2048, step=256, label="📏 Max Tokens", info="Maximum response length", ) solve_btn = gr.Button("🚀 Solve", variant="primary", size="lg") gr.Markdown("### 💡 Solution") solution_output = gr.Markdown(elem_classes=["solution-box"]) # Example problems gr.Markdown("### 📋 Quick Examples") gr.Examples( examples=[ ["""Consider the following 2x2 game:\n\nPlayer 1 \\ Player 2 | Left | Right\n--- | --- | ---\nUp | (3, 2) | (0, 4)\nDown | (1, 3) | (2, 1)\n\nFind all Nash Equilibria (pure and mixed)."""], ["""Three firms compete in a Cournot oligopoly. Market demand is P = 100 - Q where Q = q1 + q2 + q3. Each firm has marginal cost c = 10 and no fixed costs. Find the Nash Equilibrium quantities and profits."""], ["""Consider a first-price sealed-bid auction with 3 bidders. Each bidder's value is drawn independently from a uniform distribution on [0, 100]. Find the Bayesian Nash Equilibrium bidding strategy."""], ["""Two players play a sequential game. Player 1 moves first choosing L or R. If L, Player 2 chooses A or B with payoffs (L,A)=(2,1) and (L,B)=(0,3). If R, Player 2 chooses C or D with payoffs (R,C)=(1,2) and (R,D)=(3,0). Find the Subgame Perfect Nash Equilibrium using backward induction."""], ["""Consider a cooperative game with 3 players {1,2,3} and characteristic function: v({})=0, v({1})=0, v({2})=0, v({3})=0, v({1,2})=6, v({1,3})=8, v({2,3})=7, v({1,2,3})=12. Compute the Shapley value for each player."""], ], inputs=[problem_input], label="Click to load an example:", ) solve_btn.click( fn=solve_problem, inputs=[problem_input, temperature_slider, max_tokens_slider], outputs=[solution_output], ) # ================================================================= # TAB 3: About # ================================================================= with gr.TabItem("ℹī¸ About", id="about"): gr.Markdown( """ ## About GameTheory-Reasoner ### What is this? GameTheory-Reasoner is an AI system trained in **two phases** to solve game theory problems with rigorous mathematical reasoning. It was trained on the **GameTheory-Bench** dataset — a collection of 2,913 computationally verified game theory problems. ### Training Pipeline | Phase | Method | Model | Description | |-------|--------|-------|-------------| | Base | — | Qwen2.5-7B-Instruct | Pre-trained foundation model | | Phase 1: **Solver** | Supervised Fine-Tuning (SFT) | [GameTheory-Solver](https://huggingface.co/2reb/GameTheory-Solver) | Fine-tuned on verified solutions with LoRA adapters | | Phase 2: **Reasoner** | GRPO (RL) | [GameTheory-Reasoner](https://huggingface.co/Alogotron/GameTheory-Reasoner) | Reinforcement learning with verifiable rewards for reasoning quality | ### Benchmark Results: Base → Solver → Reasoner | Metric | Base (Qwen2.5-7B) | Solver (Phase 1 SFT) | Reasoner (Phase 2 GRPO) | |--------|:------------------:|:---------------------:|:-----------------------:| | **Overall Accuracy** | 82% | **94%** | **94%** | | **Hard Problems** | 66.7% | 94.4% | **94.4%** | | **Reasoning Quality** | 0.48 | 0.51 | **0.54 (+6%)** | ### Per-Category Breakdown | Category | Base | Solver | Reasoner | |----------|:----:|:------:|:--------:| | 🎲 Normal Form 2×2 | 100% | 100% | 100% | | 🎲 Normal Form 3×3 | 100% | 100% | 100% | | 🎲 Normal Form 3×4 | 80% | 80% | 80% | | 🎲 Normal Form 4×4 | 80% | 80% | 80% | | ⚔ī¸ Zero-Sum Games | 100% | 100% | 100% | | đŸŒŗ Sequential Games | 100% | 100% | 100% | | 🔨 Auction Theory | 80% | 100% | 100% | | 🔮 Bayesian Games | 0% | 100% | 100% | | 🤝 Cooperative Games | 80% | 80% | 80% | | ⚙ī¸ Mechanism Design | 60% | 100% | 100% | ### Phase 2: GRPO with Verifiable Rewards The Reasoner model was trained using **Group Relative Policy Optimization (GRPO)**, a reinforcement learning method that: - Generates multiple solution candidates per problem - Scores each using **verifiable reward functions** (answer correctness, format compliance, reasoning quality) - Updates the policy to favor higher-quality reasoning chains - Achieves the same 94% accuracy as the Solver while producing **+6% better reasoning quality** (measured by structured reasoning metrics) ### Supported Problem Types | Category | Description | Examples | |----------|------------|----------| | 🎲 Normal Form Games | Strategic form games with payoff matrices | 2×2, 3×3, 3×4, 4×4 games | | ⚔ī¸ Zero-Sum Games | Strictly competitive games | Minimax, saddle points, mixed strategies | | đŸŒŗ Sequential Games | Extensive form games with move order | Backward induction, subgame perfection | | 🔨 Auction Theory | Bidding and mechanism problems | First/second price, Dutch, English auctions | | 🔮 Bayesian Games | Incomplete information games | BNE, type spaces, belief updating | | 🤝 Cooperative Games | Coalition-based games | Shapley value, core, nucleolus | | ⚙ī¸ Mechanism Design | Incentive design problems | VCG, revelation principle, IC constraints | ### How It Works - **Browse Examples tab:** Shows pre-loaded problems from the dataset with verified solutions - **Solve Your Own tab:** Sends your problem to Qwen2.5-7B-Instruct via the HuggingFace Inference API with the GameTheory-Reasoner system prompt ### Links - 🤖 [Reasoner Model (Phase 2)](https://huggingface.co/Alogotron/GameTheory-Reasoner) - 🤗 [Solver Model (Phase 1)](https://huggingface.co/2reb/GameTheory-Solver) - 📊 [Dataset on HuggingFace](https://huggingface.co/datasets/2reb/GameTheory-Bench) """ ) # Load initial example on start app.load( fn=on_category_change, inputs=[category_dd], outputs=browse_outputs + [example_slider], ) return app # --------------------------------------------------------------------------- # Main # --------------------------------------------------------------------------- if __name__ == "__main__": app = build_app() app.launch()