Definition, classification and algorithm framework of reinforcement learning

Reinforcement learning (RL) is a machine learning algorithm between supervised learning and unsupervised learning. It solves problems through trial and error and learning. During training, reinforcement learning takes a series of decisions and is rewarded or punished based on the actions performed. The goal is to maximize the total reward. Reinforcement learning has the ability to learn autonomously and adapt, and can make optimized decisions in dynamic environments. Compared with traditional supervised learning, reinforcement learning is more suitable for problems without clear labels and can achieve good results in long-term decision-making problems.

The core of reinforcement learning is to enforce behavior based on the actions performed by the agent, and the agent is rewarded based on the positive impact of the action on the overall goal.

There are two main types of reinforcement learning algorithms:

Model-based and model-free learning algorithms

Model-based algorithm

Model-based algorithm uses transition and reward functions to estimate the optimal policy. In model-based reinforcement learning, the agent has access to a model of the environment, i.e., the actions it needs to perform to get from one state to another, the attached probabilities, and the corresponding rewards. They allow reinforcement learning agents to plan ahead by thinking ahead.

Model-free algorithm

Model-free algorithm finds the optimal strategy when the understanding of the dynamics of the environment is very limited. There are no transitions or incentives to judge the best policy. The optimal policy is estimated directly empirically, i.e. only the interaction between the agent and the environment, without any hint of the reward function.

Model-free reinforcement learning should be applied to scenarios with incomplete environmental information, such as self-driving cars, where model-free algorithms are superior to other techniques.

The most commonly used algorithm framework for reinforcement learning

Markov Decision Process (MDP)

Markov Decision Process is a reinforcement learning algorithm that provides us with a way to formalize sequential decision-making. This formalization is the basis for the problems that reinforcement learning solves. The component involved in a Markov Decision Process (MDP) is a decision maker called an agent, which interacts with its environment.

At each timestamp, the agent will obtain some representation of the state of the environment. Given this representation, the agent chooses an action to perform. The environment then transitions to some new state and the agent is rewarded for its previous actions. The important thing to note about the Markov decision process is that it does not worry about immediate rewards, but rather aims to maximize the total reward over the entire trajectory.

Bellman equation

The Bellman equation is a type of reinforcement learning algorithm that is particularly suitable for deterministic environments. The value of a given state is determined by the maximum action that the agent can take in the state it is in. The purpose of an agent is to choose actions that will maximize value.

Therefore, it needs to increase the reward of the best action in the state and add a discount factor that reduces its reward over time. Every time the agent takes an action, it returns to the next state.

Instead of summing over multiple time steps, this equation simplifies the calculation of the value function, allowing us to find the optimal solution by decomposing the complex problem into smaller recursive sub-problems. Best solution.

Q-Learning

Q-Learning combines a value function, quality based on the best possible strategy given the current state and the agent has The expected future value is assigned to the state-action pair as Q. Once the agent learns this Q-function, it looks for the best possible action that produces the highest quality in a specific state.

Through the optimal Q function, the optimal strategy can be determined by applying a reinforcement learning algorithm to find the action that maximizes the value of each state.

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