Create README.md

README.md

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+---
+library_name: stable-baselines3
+tags:
+- reinforcement-learning
+- defi
+- arbitrage
+- ppo
+- ethereum
+- l2
+---
+
+# Arb Chameleon: PPO Cross-DEX Arbitrage Agent
+
+This model is a Reinforcement Learning agent trained to identify and execute atomic price spreads across decentralized exchanges (DEXs). It uses Proximal Policy Optimization (PPO) to make trading decisions in a simulated environment that accounts for gas volatility, slippage, and transaction fees.
+
+## Model Details
+
+- **Model Type:** Proximal Policy Optimization (PPO)
+- **Library:** [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3)
+- **Environment:** Custom Gymnasium environment (`ArbEnv`) simulating multi-DEX arbitrage.
+- **Goal:** Maximize net profit while minimizing failed transactions and gas waste.
+
+## Intended Use
+
+- **Research & Education:** Understanding how RL can be applied to DeFi arbitrage.
+- **Simulation:** Testing trading strategies in a controlled, realistic environment.
+- **Inspiration:** A starting point for building more complex, production-ready trading bots.
+
+## Limitations & Risks
+
+- **Market Dynamics:** The model is trained on historical and simulated data. Real-world market conditions can change rapidly.
+- **Gas Costs:** While the model considers gas, sudden spikes in network congestion can lead to unprofitable trades if not handled by the execution layer (e.g., Flashbots).
+- **Execution Risks:** This model only provides the strategy logic. The actual execution layer (smart contracts) must be robust and secure.
+
+## Training Data
+
+The agent was trained on a universe of assets including ETH, BTC, and various stablecoins across major L1 and L2 chains (Ethereum, Arbitrum, Base, Polygon).
+
+## How to Use
+
+To load and use this model, you will need the `Arb-Chameleon` repository and the following dependencies:
+
+```bash
+pip install stable-baselines3 gymnasium numpy torch
+
+from stable_baselines3 import PPO
+# Note: You need the project's source code to define the environment
+# from rl.src.env import ArbEnv 
+
+# Load the model weights
+model = PPO.load("final_model.zip")
+
+For more details and the full source code, visit the - **GitHub Repository**: [Arb-Chameleon](https://github.com/sdi1400258/Arb-Chameleon)