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DDPG
DDPG is a milestone work for reinforcement learning as well. The major contribution of DDPG is that it introduces the actor-critic method, which directly output an action instead of searching through an action. But it uses the experience reply and fixed Q-target as well, which is a a great evidence of the importance of these two tricks as well. DDPG makes the application of reinforcement learning in nlp, recommender system or other practical commercial area achievable, although there are still many problems existing now.
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Pros and Cons of Actor-Critic Method
DQN
DDPG
Role of the Critic in Advantage Function Calculation
Robotic Chef Learning Paradigm
An autonomous agent is at a specific position in a grid world and must choose one of four directions to move (up, down, left, right). A purely value-based agent would estimate the long-term value of moving in each of the four directions and deterministically choose the direction with the highest estimated value. How does the decision-making process of an agent using an actor-critic method fundamentally differ in this same situation?
Definition of the Advantage Function
Training of Reward Models
In a reinforcement learning framework that separates the decision-making process from the evaluation process, there are two key components. Match each component to its primary function and the nature of its output.
Advantage Actor-Critic (A2C) Method