In the previous chapter, we saw the notable success of deep Q-learning (DQN) in training an AI agent to play Atari games. One limitation of DQN is that the action space must be discrete, namely, only a finite number of actions are available for the agent to select and the total number of actions cannot be too large. However, many practical tasks require continuous actions, which makes DQN difficult to apply. A naive remedy for DQN in this case is discretizing the continuous action space. But this remedy doesn't work due to the curse of dimensionality, meaning that DQN quickly becomes infeasible and does not generalize well.
This chapter will discuss deep reinforcement learning algorithms for control tasks with a continuous action space. Several classic control tasks, such as CartPole, Pendulum, and Acrobot, will be introduced first. You will...
