ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter1/learning-from-experience/demo.py

@@ -0,0 +1,217 @@
+#!/usr/bin/env python3
+"""
+Interactive demo to play the game manually or watch agents play.
+"""
+
+import os
+import sys
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Add parent directory to path for imports
+sys.path.append(str(Path(__file__).parent))
+
+from game_environment import TreasureHuntGame
+from rl_agent import QLearningAgent
+from llm_agent import LLMAgent
+
+
+def play_manual():
+    """Let the user play the game manually."""
+    print("\n" + "="*60)
+    print("MANUAL PLAY MODE")
+    print("="*60)
+    print("\nYou are playing the treasure hunt game!")
+    print("Try to find the dragon's treasure by exploring and discovering hidden mechanics.")
+    
+    game = TreasureHuntGame()
+    
+    while not game.game_over:
+        print("\n" + "-"*40)
+        print(game.get_state_description())
+        print("\nAvailable actions:")
+        actions = game.get_available_actions()
+        for i, action in enumerate(actions, 1):
+            print(f"  {i}. {action}")
+        
+        # Get user input
+        choice = input("\nEnter action number or type custom action: ").strip()
+        
+        # Parse input
+        if choice.isdigit() and 1 <= int(choice) <= len(actions):
+            action = actions[int(choice) - 1]
+        else:
+            action = choice
+        
+        # Execute action
+        feedback, reward, done = game.execute_action(action)
+        print(f"\nFeedback: {feedback}")
+        print(f"Reward: {reward:.2f}")
+    
+    if game.victory:
+        print("\n🎉 CONGRATULATIONS! You won!")
+    else:
+        print("\n💀 GAME OVER! Better luck next time.")
+    
+    print(f"Final score: {game.score}")
+
+
+def watch_rl_agent():
+    """Watch a trained RL agent play."""
+    print("\n" + "="*60)
+    print("WATCHING Q-LEARNING AGENT")
+    print("="*60)
+    
+    # Check if trained agent exists
+    agent_path = Path("results") / "rl_agent_demo.pkl"
+    
+    agent = QLearningAgent()
+    
+    if agent_path.exists():
+        print("Loading pre-trained agent...")
+        agent.load(agent_path)
+    else:
+        print("No pre-trained agent found. Training one now...")
+        print("This will take a few minutes...\n")
+        
+        game = TreasureHuntGame()
+        agent.train(num_episodes=2000, verbose=True)
+        
+        # Save for future use
+        agent_path.parent.mkdir(exist_ok=True)
+        agent.save(agent_path)
+    
+    # Watch agent play
+    print("\nWatching agent play...")
+    game = TreasureHuntGame()
+    total_reward = 0
+    steps = 0
+    
+    while not game.game_over:
+        print("\n" + "-"*40)
+        print(game.get_state_description())
+        
+        action = agent.choose_action(game, training=False)
+        print(f"\nAgent chooses: {action}")
+        
+        feedback, reward, done = game.execute_action(action)
+        print(f"Feedback: {feedback}")
+        print(f"Reward: {reward:.2f}")
+        
+        total_reward += reward
+        steps += 1
+        
+        input("\nPress Enter to continue...")
+    
+    if game.victory:
+        print("\n🎉 Agent won!")
+    else:
+        print("\n💀 Agent failed.")
+    
+    print(f"Total reward: {total_reward:.2f}")
+    print(f"Steps taken: {steps}")
+
+
+def watch_llm_agent():
+    """Watch an LLM agent play with reasoning."""
+    print("\n" + "="*60)
+    print("WATCHING LLM AGENT (with reasoning)")
+    print("="*60)
+    
+    # Check API key
+    provider = os.getenv("LLM_PROVIDER", "moonshot").lower()
+    api_key = os.getenv("DASHSCOPE_API_KEY") if provider in {"dashscope", "qwen", "bailian"} else os.getenv("MOONSHOT_API_KEY")
+    if not api_key and not os.getenv("OPENROUTER_API_KEY"):
+        print(f"\nError: API key for provider '{provider}' not set.")
+        print("Please set your Kimi API key:")
+        print("  export DASHSCOPE_API_KEY='your-key-here'  # for dashscope/qwen/bailian")
+        print("  export MOONSHOT_API_KEY='your-key-here'   # for moonshot/kimi")
+        print("Or set OPENROUTER_API_KEY as a universal fallback.")
+        return
+    
+    agent = LLMAgent(api_key=api_key, provider=provider)
+    
+    # Load experiences if available
+    exp_path = Path("results") / "llm_experiences_demo.json"
+    if exp_path.exists():
+        print("Loading previous experiences...")
+        agent.load_experiences(exp_path)
+        print(f"Loaded {len(agent.experiences)} experiences")
+    
+    # Play one episode with verbose output
+    print("\nWatching LLM agent play with reasoning...")
+    print("(The agent will explain its thought process)\n")
+    
+    game = TreasureHuntGame()
+    reward, steps, victory = agent.play_episode(game, verbose=True)
+    
+    if victory:
+        print("\n🎉 LLM agent won!")
+    else:
+        print("\n💀 LLM agent failed.")
+    
+    print(f"Total reward: {reward:.2f}")
+    print(f"Steps taken: {steps}")
+    print(f"API calls made: {agent.api_calls}")
+    
+    # Save experiences
+    exp_path.parent.mkdir(exist_ok=True)
+    agent.save_experiences(exp_path)
+
+
+def show_hidden_rules():
+    """Reveal the hidden game mechanics."""
+    print("\n" + "="*60)
+    print("HIDDEN GAME MECHANICS (SPOILERS!)")
+    print("="*60)
+    
+    game = TreasureHuntGame()
+    print(game.get_hidden_rules())
+    
+    print("\nThese are the rules that agents must discover through experience.")
+    print("Traditional RL requires thousands of episodes to learn these patterns,")
+    print("while LLMs can often figure them out in just 20-30 episodes through reasoning.")
+
+
+def main():
+    """Main menu for the demo."""
+    while True:
+        print("\n" + "="*70)
+        print("LEARNING FROM EXPERIENCE DEMO")
+        print("Comparing RL vs LLM In-Context Learning")
+        print("="*70)
+        
+        print("\nChoose an option:")
+        print("1. Play the game manually")
+        print("2. Watch Q-Learning agent play (pre-trained)")
+        print("3. Watch LLM agent play with reasoning")
+        print("4. Show hidden game mechanics (spoilers!)")
+        print("5. Run full experiment")
+        print("6. Exit")
+        
+        choice = input("\nEnter your choice (1-6): ").strip()
+        
+        if choice == "1":
+            play_manual()
+        elif choice == "2":
+            watch_rl_agent()
+        elif choice == "3":
+            watch_llm_agent()
+        elif choice == "4":
+            show_hidden_rules()
+        elif choice == "5":
+            print("\nRunning full experiment...")
+            os.system("python experiment.py")
+        elif choice == "6":
+            print("\nGoodbye!")
+            break
+        else:
+            print("\nInvalid choice. Please try again.")
+
+
+if __name__ == "__main__":
+    main()