Deep Deterministic Policy Gradient
Deep Deterministic Policy Gradient (DDPG) is a model-free, off-policy reinforcement learning algorithm that combines deep learning with deterministic policy gradients to handle continuous action spaces. It uses an actor-critic architecture where the actor network outputs deterministic actions, and the critic network evaluates the action-value function, enabling stable learning in high-dimensional environments. DDPG is particularly effective for tasks requiring precise control, such as robotics, autonomous driving, and complex game environments.
Developers should learn DDPG when working on reinforcement learning projects involving continuous action spaces, as it addresses the limitations of traditional Q-learning methods that struggle with high-dimensional outputs. It is ideal for applications like robotic manipulation, where actions are real-valued (e.g., joint angles or velocities), and in simulation-based training for autonomous systems. DDPG's off-policy nature allows for efficient sample reuse, making it suitable for environments with expensive data collection.