Deep Q-Networks are a type of artificial intelligence that uses deep learning to enable an agent to learn optimal actions in an environment through trial and error. This technique combines reinforcement learning with deep neural networks, allowing the agent to approximate the optimal action-value function, known as the Q-function. The network learns to predict the expected reward for taking a particular action in a given state, guiding the agent toward behaviors that maximize cumulative rewards over time. This approach has proven effective in complex decision-making scenarios.
Context
Deep Q-Networks hold potential for optimizing decentralized finance (DeFi) protocols, automated trading algorithms, and network resource allocation within blockchain systems. The current state of development involves adapting these sophisticated learning models to the unique constraints and data structures of decentralized environments. Future applications may include self-optimizing smart contracts and autonomous market makers.
A new Deep Reinforcement Learning model dynamically selects validators and adjusts difficulty, fundamentally solving the scalability-latency trade-off.
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