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environments/community/pay_to_play/README.md
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@ -144,15 +144,15 @@ env = PayToPlayEnv(config, server_configs, testing=True)
```python
async def training_loop():
await env.setup()
for step in range(config.total_steps):
# Get next question
question = await env.get_next_item()
# Agent selects agent cards and makes payments
# Evaluates response and gets training signal
scored_data, _ = await env.collect_trajectories(question)
# Log metrics
await env.wandb_log()
```
@ -162,7 +162,7 @@ async def training_loop():
### Pricing Strategy
- **Technical Expert ($0.03)**: Premium pricing reflects high accuracy and specialized knowledge
- **Communication Specialist ($0.02)**: Mid-tier pricing for clarity and accessibility focus
- **Communication Specialist ($0.02)**: Mid-tier pricing for clarity and accessibility focus
- **Creative Thinker ($0.01)**: Budget option encouraging creativity and innovation
### Budget Scenarios
@ -190,7 +190,7 @@ async def _agent_select_judges(self, question: str) -> JudgeSelection:
# 1. Agent analyzes question directly (no pre-categorization)
# 2. Get agent card performance stats
judge_stats = self._get_judge_performance_stats()
# 3. AI agent makes strategic decision with full context
selection_response = await self.server.chat_completion(
messages=[
@ -198,7 +198,7 @@ async def _agent_select_judges(self, question: str) -> JudgeSelection:
{"role": "user", "content": selection_prompt}
]
)
# 4. Validate and execute selection
return validated_selection
```
@ -285,19 +285,19 @@ Set `testing_mode=False` for real USDC payments on Base blockchain:
## 🎖️ Key Features
✅ **Multiple Specialized Agent Cards**: Different expertise areas and pricing tiers
✅ **Intelligent Agent Selection**: AI-driven agent card selection with dynamic question analysis
✅ **Budget Awareness**: Real economic constraints drive efficient learning
✅ **Performance Tracking**: Historical data informs future decisions
✅ **Blockchain Integration**: Real USDC payments on Base network
✅ **Comprehensive Monitoring**: Detailed metrics and decision analysis
✅ **Fallback Mechanisms**: Robust handling of budget constraints
✅ **Testing Framework**: Simulation mode for development and testing
✅ **Multiple Specialized Agent Cards**: Different expertise areas and pricing tiers
✅ **Intelligent Agent Selection**: AI-driven agent card selection with dynamic question analysis
✅ **Budget Awareness**: Real economic constraints drive efficient learning
✅ **Performance Tracking**: Historical data informs future decisions
✅ **Blockchain Integration**: Real USDC payments on Base network
✅ **Comprehensive Monitoring**: Detailed metrics and decision analysis
✅ **Fallback Mechanisms**: Robust handling of budget constraints
✅ **Testing Framework**: Simulation mode for development and testing
## 🚧 Future Enhancements
- **Dynamic Pricing**: Agent card prices adjust based on demand and performance
- **Agent Card Reputation System**: Community-driven agent card quality ratings
- **Agent Card Reputation System**: Community-driven agent card quality ratings
- **Multi-Round Evaluation**: Iterative feedback and improvement cycles
- **Agent Card Specialization**: More granular specialty categories
- **Economic Incentives**: Reward mechanisms for high-performing agent cards
@ -327,4 +327,3 @@ This environment builds upon recent advances in reinforcement learning from AI f
- **RLAIF vs. RLHF**: Lee, H., et al. (2023). "RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback." *arXiv preprint arXiv:2309.00267*. [https://arxiv.org/abs/2309.00267](https://arxiv.org/abs/2309.00267)
- **Mixture of Judges**: Xu, T., et al. (2024). "The Perfect Blend: Redefining RLHF with Mixture of Judges." *arXiv preprint arXiv:2409.20370*. [https://arxiv.org/abs/2409.20370](https://arxiv.org/abs/2409.20370)