Q-learning
Q-learning is a reinforcement learning algorithm whose main purpose is to maximize long-term rewards by learning how to make optimal decisions. In Q-learning, we use a table called Q-table to save the value functions of the available actions in each state. This value function represents the long-term reward expected from taking a specific action.
The workflow of Q-learning is as follows:
- Initialize Q-table: assign an initial value to all combinations of states and actions.
- Select an action: Use some strategy (such as ε-greedy method) to select an action from the current state.
- Perform actions and observe the results: Perform the chosen action and observe feedback from the environment as well as new statuses and rewards.
- Update Q-table: Use update rules to update the value function of the corresponding state-action pair in the Q-table.
Repeat steps 2 to 4 until the task end condition is reached.
Q-learning has some advantages and disadvantages. The advantages include: it has good convergence, does not require prior knowledge, and can handle continuous state and action space problems, etc. Disadvantages include that it requires more computing resources and time than other algorithms, and it is easy to fall into local optimal solutions.
Although Q-learning has some limitations and challenges, it is still a very useful and popular reinforcement learning algorithm. In practical applications, we can optimize the performance of Q-learning by adjusting relevant parameters and using reward functions.
DQN
DQN (Deep Q-Network) is a reinforcement learning algorithm based on deep learning. It uses a neural network to approximate the Q function and solves the limitations of Q-learning in dealing with high-dimensional state space problems. The main idea of the DQN algorithm is to use neural networks to approximate the Q-value function and adopt some strategies to balance exploration and development.
The basic process of DQN is as follows:
- Initialize the Experience Replay Buffer and Deep Neural Network.
- Use some strategy (such as the ε-greedy method) to select an action from the current state.
- Perform selected actions and observe feedback from the environment as well as new statuses and rewards.
- Store the experience of this action in the experience replay cache, including the current state, action, reward and next state.
- Randomly sample a batch of experiences from the experience replay cache and use the target Q-value update rule to train the deep neural network.
Repeat steps 2 to 5 until the task end condition is reached.
The DQN algorithm solves the problems of instability and over-fitting that are prone to occur in practical applications of Q-learning by combining the two methods of experience replay and target Q-value update.
The advantages of the DQN algorithm are that it can handle high-dimensional state space problems, has good convergence, and has strong scalability. However, DQN still has some challenges, such as slow convergence and sensitivity to hyperparameters. Therefore, in actual applications, it needs to be adjusted and optimized according to specific conditions to achieve better performance.
PPO
PPO (Proximal Policy Optimization) is a popular reinforcement learning algorithm, which is a method based on policy optimization. Unlike other algorithms based on policy optimization, PPO uses a technology called "proximal policy optimization" to control the step size of model updates, thereby avoiding performance degradation caused by over-adjustment of the policy.
The main idea of the PPO algorithm is to update the strategy in each step by maximizing the use of existing data sets, while trying to avoid too big changes to the strategy. Specifically, the PPO algorithm usually uses some techniques to control the update step size, such as truncation of importance ratio, split optimization, etc., to achieve a more stable and efficient training process.
The basic process of the PPO algorithm is as follows:
- Initialize the deep neural network and input the current state into the neural network to obtain the action probability distribution.
- Select an action from the action probability distribution according to some strategy (such as Monte Carlo tree search or ε-greedy method).
- Perform selected actions and observe feedback from the environment as well as new statuses and rewards.
- Store the experience of this action in the experience replay cache, including the current state, action, reward and next state.
- Randomly sample a batch of experiences from the experience replay cache and use the PPO algorithm to update the deep neural network.
Repeat steps 2 to 5 until the task end condition is reached.
The advantage of the PPO algorithm is that it can effectively control the range of policy changes during the training process, thereby achieving more stable and efficient learning.
PPO simplified into two steps
Collect data: By executing a series of strategies and recording corresponding status, action and reward information, a set of trajectory data is formed.
Update policy: Use the collected data to update the policy network parameters to maximize the expected reward function. Among them, PPO uses a technology called "proximal policy optimization", which prevents the update process from being too drastic by limiting the difference between the new policy and the old policy, thereby increasing the stability of the algorithm. .
In addition, PPO further improves the stability and efficiency of the algorithm by sampling multiple small batches of data and merging them into a larger training set (called "mini-batch").