Deep Reinforcement Learning

Deep RL combines function approximation with sequential decision-making to learn policies from interaction. Core algorithms include policy gradients (PPO, A3C), value-based methods (DQN), and actor-critic hybrids, with practical concerns around stability, reward shaping, and sample efficiency.