Understand how deep Q-networks work

Deep Q Network (DQN) is a reinforcement learning algorithm based on deep learning technology, specifically used to solve discrete action space problems. This algorithm was proposed by DeepMind in 2013 and is widely regarded as an important milestone in the field of deep reinforcement learning.

In the traditional Q-learning algorithm, we use a Q-table to store the value of each action in each state in order to select the optimal action by looking up the Q-table. However, when the state space and action space are very large, the storage and update of the Q table becomes difficult, which is the so-called "curse of dimensionality" problem. To solve this problem, DQN adopts a deep neural network to approximate the Q function. By training a neural network, we can take the state as input and output the corresponding Q value for each action. In this way, we can select the optimal action through the neural network and no longer need to maintain a huge Q table. The use of deep neural networks makes the Q-learning algorithm more suitable for large and complex problems and has achieved significant performance improvements.

The core idea of ​​DQN is to learn the approximation of the Q function through a neural network, with the state as input and the action as output. Specifically, DQN uses a deep convolutional neural network (CNN) to process the game state and output the Q-value of each action. Then, DQN selects actions based on a greedy strategy or a random strategy under a certain probability. At each time step, DQN passes the current state and selected action to the environment and obtains the reward and next state. Using this information, DQN updates the parameters of the neural network, gradually improving the approximation of the Q function, making it closer to the actual Q function.

The core advantage of the DQN algorithm is to learn complex strategies in high-dimensional state space and discrete action space without manually designing features and rules. In addition, DQN also has the following features:

DQN uses Experience Replay to balance exploration and utilization. Experience replay is a technology that stores and reuses previous experiences to improve training efficiency and stability. Specifically, DQN stores experience tuples (including states, actions, rewards, and next states) in a buffer, and then randomly extracts a batch of experiences from the buffer for training. This method avoids using only the latest experience each time, but uses previous experience for training, thus providing a richer sample space. Through experience replay, DQN can more effectively learn the dynamics of the environment and the long-term impact of the strategy, improving the performance and stability of the algorithm.

2. Target Network: DQN uses the Target Network to reduce the fluctuation of the objective function. Specifically, DQN uses two neural networks, one is the main network (Main Network), used to select actions and calculate the Q value; the other is the target network, used to calculate the target Q value. The parameters of the target network are updated regularly to maintain a certain difference from the main network. This can reduce the fluctuation of the objective function, thereby improving the stability and convergence speed of training.

3.Double DQN: DQN uses Double DQN to solve the estimation bias problem. Specifically, Double DQN uses the main network to select the optimal action and the target network to calculate the Q value. This reduces estimation bias and improves learning efficiency and stability.

In short, DQN is a very powerful deep reinforcement learning algorithm that can learn complex strategies in discrete action spaces and has good stability and convergence speed. It has been widely used in various fields, such as games, robot control, natural language processing, etc., and has made important contributions to the development of artificial intelligence.

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