Reinforcement Learning for Complex System Management

Reinforcement Learning (RL) is introduced as a tool for optimizing sequential decisions under uncertainty in complex systems. RL involves learning policies to maximize reward-based metrics, with applications ranging from robotics to game playing. The integration of machine learning in managing complex systems, such as using RL for smartlocks in multicore scenarios, demonstrates its potential in adaptive decision-making and optimization.

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