A beginner’s guide to reinforcement learning: providing an overview of RL and exploring some of the fundamental concepts and algorithms

Reinforcement learning is a type of machine learning that involves training an agent to interact with an environment in order to maximize a reward signal. This can be a powerful approach for solving complex problems in areas like robotics, gaming AI, and finance, but it can also be challenging to get started with if you’re new to the field.

In this article, we’ll provide a beginner’s guide to reinforcement learning, exploring some of the fundamental concepts and algorithms that underpin this exciting area of study.

The reinforcement learning problem

At a high level, the reinforcement learning problem can be thought of as follows: an agent interacts with an environment by taking actions, and the environment responds by providing the agent with rewards or penalties. The goal of the agent is to learn a policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time.

Markov decision processes

To formalize the reinforcement learning problem, we use a mathematical framework called a Markov decision process (MDP). An MDP consists of a set of states, actions, and rewards, as well as a transition function that specifies the probability of transitioning from one state to another when a given action is taken.

Value functions

A key concept in reinforcement learning is that of a value function, which assigns a value to each state or state-action pair in the MDP. There are two types of value functions: state value functions, which estimate the expected cumulative reward starting from a given state, and action-value functions, which estimate the expected cumulative reward starting from a given state-action pair.

Bellman equations

To calculate the value function of an MDP, we can use a set of recursive equations called the Bellman equations. These equations express the value of a state or state-action pair in terms of the values of its successor states or state-action pairs, and they provide a powerful tool for solving MDPs.

Q-learning

One of the most popular and powerful reinforcement learning algorithms is Q-learning. Q-learning is an iterative algorithm that estimates the optimal action-value function of an MDP, and it has been used to achieve state-of-the-art results in a wide range of applications.

Policy gradients

Another approach to reinforcement learning is to directly optimize a policy for maximizing rewards, rather than estimating a value function. This approach is known as "policy gradients," and it has become increasingly popular in recent years due to its ability to handle high-dimensional state spaces and non-differentiable policies.

Deep reinforcement learning

One of the most exciting recent developments in reinforcement learning has been the integration of deep neural networks. By using deep neural networks to represent the value or policy functions in RL, researchers have been able to achieve remarkable performance on challenging tasks like playing Atari games and controlling robots.

Applications of RL

Reinforcement learning has a wide range of applications, from gaming AI to robotics to finance. Some examples of RL applications include training autonomous drones to navigate complex environments, optimizing supply chain management systems, and developing algorithms for automatically trading stocks.

Conclusion

Reinforcement learning is an exciting and rapidly evolving field that offers a powerful approach to solving complex problems in a wide range of domains. By understanding the fundamental concepts and algorithms of RL, you can start to explore the many exciting applications and possibilities of this field.