AI engineering glossary · Reinforcement learning
What is DDPG (deep deterministic policy gradient)?
Deep deterministic policy gradient (also called DDPG): An off-policy actor-critic method for continuous actions that pairs a deterministic actor with a Q-value critic, uses a replay buffer and slowly updated target networks, and adds noise to actions for exploration.
Deep deterministic policy gradient: an example
Training a simulated robot to reach a target by outputting joint torques plus Gaussian noise.
Related terms
More Reinforcement learning terms
- Deep Q-network
- Deterministic policy
- Deterministic policy gradient
- Discount factor
- Distributional reinforcement learning
- Double DQN
- Double Q-learning
- Dueling network
Where you learn it
Deep deterministic policy gradient is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.