Reinforcement Learning
Reinforcement Learning is a paradigm of Machine Learning where an autonomous agent learns to make decisions by performing actions in an Environment. The goal is to maximize a cumulative Reward signal. Unlike supervised learning, the agent is not told which actions to take but must discover which actions yield the most reward by trying them, a concept known as the Exploration-Exploitation Tradeoff. This mathematical framework is typically structured as a Markov Decision Process (MDP).
Core Algorithms and Paradigms
The field is broadly divided into model-based and model-free approaches. Q-Learning is a popular model-free, value-based algorithm that learns the value of actions in specific states. The introduction of Deep Learning led to the creation of Deep Q-Networks (DQN), which achieved human-level performance on Atari games as documented by DeepMind. Other significant methods include Policy Gradients, Actor-Critic Models, and Proximal Policy Optimization (PPO), which are widely used in Robotics and complex simulations.
Theoretical Foundations and Resources
The theoretical foundations of the field were largely established by Richard Sutton and Andrew Barto in their seminal textbook. Modern implementations often utilize libraries from OpenAI, such as Gym or Spinning Up, to benchmark agent performance. For a deep dive into the mathematics of Temporal Difference Learning or Monte Carlo Methods, researchers often cite the Introduction to Deep Reinforcement Learning.