Deep Deterministic Policy Gradient (DDPG) is a reinforcement learning technique that combines both Q-learning and Policy gradients. DDPG being an actor-critic technique consists of two models: Actor and Critic. The actor is a policy network that takes the state as input and outputs the exact action (continuous), instead of a probability distribution over actions. The critic is a Q-value network that takes in state and action as input and outputs the Q-value. DDPG is an “off”-policy method. DDPG is used in the continuous action setting and the “deterministic” in DDPG refers to the fact that the actor computes the action directly instead of a probability distribution over actions.
DDPG is used in a continuous action setting and is an improvement over the vanilla actor-critic.
- https://towardsdatascience.com/deep-deterministic-policy-gradients-explained-2d94655a9b7b
- https://spinningup.openai.com/en/latest/algorithms/ddpg.html
- https://keras.io/examples/rl/ddpg_pendulum/
- https://www.youtube.com/watch?v=4jh32CvwKYw&t=903s
- https://towardsdatascience.com/deep-deterministic-policy-gradient-ddpg-theory-and-implementation-747a3010e82f