Deep Deterministic Policy Gradient (DDPG) is an off-policy, model-free reinforcement learning algorithm that is used for continuous action spaces. DDPG combines the strengths of Q-learning and policy gradient methods to learn a deterministic policy in environments with continuous action spaces. It is based on the actor-critic architecture, where the actor learns the policy, and the critic evaluates it.
DDPG uses a deep neural network (typically a multi-layer perceptron) to approximate both the Q-value function (critic) and the policy function (actor). It also employs experience replay to store past experiences and sample from them during training, which helps stabilize learning. In addition, DDPG utilizes target networks—separate networks used to compute target Q-values and stabilize the training process.
DDPG is particularly effective in tasks like robotic control, where the action space is continuous (e.g., controlling the joints of a robot arm), and has been used successfully in environments such as OpenAI Gym and MuJoCo.