{"id":7855,"date":"2025-11-27T16:03:35","date_gmt":"2025-11-27T16:03:35","guid":{"rendered":"https:\/\/uplatz.com\/blog\/?p=7855"},"modified":"2025-11-27T16:03:35","modified_gmt":"2025-11-27T16:03:35","slug":"reinforcement-learning-dqn-ppo-alphazero-explained","status":"publish","type":"post","link":"https:\/\/uplatz.com\/blog\/reinforcement-learning-dqn-ppo-alphazero-explained\/","title":{"rendered":"Reinforcement Learning (DQN, PPO, AlphaZero) Explained"},"content":{"rendered":"

Reinforcement Learning (DQN, PPO, AlphaZero): How AI Learns Through Reward and Experience<\/strong><\/h1>\n

Most machine learning models learn from data that already exists. Reinforcement Learning (RL) is different. It allows AI systems to learn by doing<\/strong>, just like humans. Instead of labelled datasets, the model learns by interacting with an environment, making decisions, and receiving rewards or penalties.<\/p>\n

This learning method powers robot control, autonomous vehicles, game-playing AI, recommendation engines, and real-time decision systems<\/strong>. Some of the most famous RL systems include DQN<\/strong>, PPO<\/strong>, and AlphaZero<\/strong>.<\/p>\n

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1. What Is Reinforcement Learning?<\/strong><\/h2>\n

Reinforcement Learning is a type of machine learning where an agent<\/strong> learns how to act in an environment<\/strong> to maximise a reward<\/strong>.<\/p>\n

Instead of learning from examples, the agent learns through:<\/p>\n