Reinforcement Learning (RL) is a machine learning paradigm in which an agent learns to make decisions by interacting with an environment to maximize a cumulative reward signal. Unlike supervised learning, where correct answers serve as training signals, RL operates under uncertainty: the agent must balance exploration of new strategies with exploitation of known successes. The theoretical foundations are Markov Decision Processes (MDPs) and the Bellman equation, which formalize the relationship between the value of a state and its reachable successor states. Classical tabular methods such as Q-Learning, Temporal Difference Learning, and Monte Carlo methods solve these equations iteratively for finite state spaces.
Deep Reinforcement Learning extends these foundations by using neural networks as function approximators, enabling application to high-dimensional and continuous state spaces. Deep Q-Networks (DQN) achieved the breakthrough for discrete action spaces, while algorithms like Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor-Critic (SAC) brought RL into continuous action spaces—from precise robotic arm control to algorithmic portfolio optimization. Modern training environments such as Gymnasium (formerly OpenAI Gym) and the freely available physics simulation MuJoCo form the standard ecosystem for development and benchmarking. The combination of Actor-Critic architectures, replay buffers, and techniques like clipped double-Q learning has made Deep RL one of the most dynamic research fields in AI—with direct applications in robotics, autonomous driving, game theory, and financial markets.
Recommended reference: Deep Reinforcement Learning in PyTorch (DDPG, TD3, SAC) – Lazy Programmer