Reinforcement Learning Approach
Reinforcement learning (RL) is a machine learning paradigm focused on training agents to make optimal decisions in dynamic environments by maximizing cumulative rewards. Current research emphasizes applying RL to diverse problems, including optimizing complex processes (e.g., space mission planning, manufacturing, traffic control), often employing algorithms like Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN) with neural network architectures. This approach offers significant potential for improving efficiency and adaptability in various fields, from robotics and resource management to financial modeling and healthcare.
Papers
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