Deterministic policy gradient
As discussed in the previous chapter, DQN uses the Q-network to estimate the state-action value function, which has a separate output for each available action. Therefore, the Q-network cannot be applied, due to the continuous action space. A careful reader may remember that there is another architecture of the Q-network that takes both the state and the action as its inputs, and outputs the estimate of the corresponding Q-value. This architecture doesn't require the number of available actions to be finite, and has the capability to deal with continuous input actions:
If we use this kind of network to estimate the state-action value function, there must be another network that defines the behavior policy of the agent, namely outputting a proper action given the observed state. In fact, this is the intuition behind actor-critic reinforcement learning algorithms. The actor-critic architecture contains two parts:
- Actor: The actor defines the behavior policy of the...
