Deep Reinforcement Learning: The Actor-Critic Method

Actor-Critic reinforcement learning improves over traditional REINFORCE methods by enabling continuous learning during episodes, achieving 68% success rate in half the training time (600 vs 1200 iterations) on a drone landing task. The method uses two neural networks - an actor that controls actions and a critic that evaluates states - providing immediate feedback through TD error calculations that measure whether actions performed better or worse than expected. Implementation requires careful attention to three critical bugs: detaching gradients in TD targets to prevent the "moving target problem," setting appropriate discount factors so terminal rewards remain visible, and designing reward functions that track state transitions rather than snapshots to prevent exploitation behaviors.