Adaptive Reinforcement Learning Algorithm for Dynamic Environments

The patent applies dynamics by making the reinforcement learning algorithm adaptive to environment changes. The algorithm dynamically modifies its parameters (learning rate, exploration rate, discount factor) based on detected environmental changes, allowing it to adapt quickly without requiring extensive retraining. This resolves the contradiction by enabling the agent to maintain high decision-making speed while adapting to dynamic environments through parameter adjustment rather than full retraining.

The patent directly applies parameter changes by modifying key RL algorithm parameters (learning rate α, exploration rate ε, discount factor γ) in response to environmental changes. When the environment is detected to have changed, the algorithm adjusts these parameters to optimize performance for the new conditions. This approach allows quick adaptation without the need for extensive data collection and retraining, thus resolving the time loss contradiction.

The patent applies feedback by continuously monitoring reward values and performance indicators to detect environmental changes. Based on this feedback, the algorithm automatically adjusts its parameters to maintain optimal performance. The feedback mechanism includes comparing current performance against thresholds and triggering parameter modifications when changes are detected, enabling adaptability through a structured yet relatively simple feedback loop.

The patent applies self-service by enabling the reinforcement learning algorithm to automatically detect environmental changes and adjust its own parameters without external intervention. The algorithm monitors its own performance metrics and autonomously modifies its learning rate, exploration rate, and discount factor based on detected changes, reducing the need for complex external control systems while maintaining high adaptability.