The Actor-Critic Algorithm (AC) is a reinforcement learning algorithm that combines a policy network and a value function to calculate the probability of different actions being taken in different states based on the reward and punishment information of the results.

The behavior-critic algorithm designs two neural networks, each time updating the parameters in a continuous state, and there is a correlation before and after each parameter update. Compared with the traditional policy network, it has better learning efficiency and performance, but it is prone to bias and can only produce local optimal solutions.

AC Algorithm Advantages

• Better convergence
• Higher dimensions and continuous action spaces work better
• Stochastic strategy can be used

Disadvantages of AC algorithm

• Usually the local optimal solution is obtained
• Evaluation strategies are inefficient and have high bias