Deep Reinforcement Learning

Deep Reinforcement Learning #

Deep Reinforcement Learning (DRL) studies how an agent learns to make a sequence of decisions by interacting with an environment and receiving feedback through rewards.

Reinforcement learning provides the framework for sequential decision-making. Deep learning extends this framework with powerful function approximators that can handle large or complex state and action spaces.

Deep Reinforcement Learning = Reinforcement Learning + Deep Neural Networks

Classical reinforcement learning foundations → progresses towards → value-based deep learning → policy-gradient methods → model-based approaches → imitation learning.