3️⃣DDPG

DDPG (Deep Deterministic Policy Gradient) is a model-free, online, off-policy reinforcement learning algorithm. It combines the actor-critic architecture with the deterministic policy gradient (DPG) algorithm and is used to learn continuous actions.

The main idea behind DDPG is to use a deep neural network, called the actor network, to learn the policy function, which maps states to actions. The actor network is trained to maximize the expected cumulative reward. The algorithm also uses a second neural network, called the critic network, to learn the action-value function. The critic network is used to provide a value for the policy function, which is used to update the actor network.

DDPG uses two key techniques to improve the stability and performance of the algorithm:

• Experience replay: DDPG uses a replay buffer to store the experiences of the agent. This allows the agent to learn from previous experiences and helps to break the correlation between consecutive samples.

• Target networks: DDPG uses target networks for both the actor and critic networks. These are fixed copies of the original networks, which are used to provide a stable target for the learning process.

DDPG is well suited for problems with high-dimensional, continuous action spaces, such as robotic control, game playing and simulations.

Analogy:

An analogy for the DDPG algorithm would be a professional golfer learning to perfect their swing.

• The golfer is the agent and the golf course is the environment.

• The different positions and angles of the golfer's body and club are the states.

• The different actions the golfer can take, such as the swing angle, swing speed and club selection, are the actions.

• The distance the ball travels and how close it is to the hole is the reward.

The golfer starts at the tee and must make a series of decisions (take actions) in order to reach the hole. They use a video analysis of their swing, along with feedback from a coach and their own experience, to improve their swing (policy function). The golfer also uses a feedback from the coach on how close the ball is to the hole (action-value function) to adjust their swing.

Just like DDPG, the golfer has a current policy of swing and is trying to perfect it by learning from previous experiences and feedback, and also uses a combination of actor and critic networks (coach and video analysis) to improve the stability and performance of the learning process.