DDPG (Deep Deterministic Policy Gradient)

Deep Deterministic Policy Gradient (DDPG) is an advanced algorithm used in reinforcement learning, which helps machines learn how to make decisions in complex environments. It combines deep learning and the principles of policy gradient methods. DDPG operates in continuous action spaces, meaning it can choose from a range of actions rather than just a few specific options. The algorithm uses two neural networks: one for determining the best actions (the actor) and another for evaluating those actions (the critic). Over time, DDPG improves its strategies by learning from experiences, much like how we learn from trial and error.