Reinforcement-Learning Agent

Avilom uses a Deep Q-Network (DQN) to optimize gas fee suggestions.

Model:

• 2 hidden layers, 128 units

• Discrete actions (e.g., suggest 10–100 Gwei)

Reward Signal:

• Balances low cost with fast confirmation time

🤖 The agent adapts to changing network states and user behavior.