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Introduction to AI

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Reinforcement Learning

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General

Introduction to Reinforcement Learning

Reinforcement Learning (RL) is a subfield of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward. Unlike supervised learning, RL does not require labeled input/output pairs.

Key Components

  • Agent: The learner or decision-maker.
  • Environment: Everything the agent interacts with.
  • State: The current situation of the agent.
  • Action: All possible moves the agent can make.
  • Reward: Feedback from the environment based on the action.
  1. Q-Learning: A model-free RL algorithm.
  2. Deep Q-Networks (DQN): Combines Q-learning with deep neural networks.
  3. Proximal Policy Optimization (PPO): A popular policy gradient method.

Real-World Applications

  • Robotics: Training robots for complex tasks.
  • Game Playing: Systems like AlphaGo.
  • Autonomous Vehicles: Navigating complex traffic scenarios.