Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
AI agents learn web navigation through guided exploration and self-critique, outperforming humans.
AI agents learn web navigation through guided exploration and self-critique, outperforming humans.
Original Problem 🔍:
LLMs struggle with agentic, multi-step reasoning in interactive environments like web navigation. Traditional supervised pre-training on static datasets falls short in enabling autonomous agent capabilities for complex decision-making in dynamic settings.
Solution in this Paper 🧠:
• Introduces Agent Q framework combining:
Guided Monte Carlo Tree Search (MCTS) for exploration
Self-critique mechanism for intermediate rewards
Iterative fine-tuning using off-policy Direct Preference Optimization (DPO)
• Allows learning from both successful and unsuccessful trajectories
• Improves generalization in complex, multi-step reasoning tasks
Key Insights from this Paper 💡:
• Combining search and learning significantly boosts agent performance
• Process-level supervision improves over purely outcome-based training
• Test-time search capabilities provide substantial performance gains
• Fine-grained credit assignment crucial for long-horizon tasks
Results 📊:
• WebShop: Outperforms baselines and average human performance (50.5% vs 50.0%)
• OpenTable (real-world booking):
Improves LLaMA-3 70B zero-shot performance from 18.6% to 81.7% success rate
With online search, further improves to 95.4% success rate
• Surpasses GPT-4's performance after one day of autonomous data collection
🧠 What are the key components of the Agent Q framework?
The key components of Agent Q are:
Guided Monte Carlo Tree Search (MCTS) for exploration
Self-critique mechanism for intermediate rewards
Iterative fine-tuning using an off-policy variant of Direct Preference Optimization (DPO)
