Within Artificial Intelligence, Reinforcement Learning is rising as a promising area.
This Machine Learning subfield allows AI to boost performance.
Its algorithms arealso key in Large Language Models (LLMs).
But what makes Reinforcement Learning special? Let’s dive deeper into that.
What is Reinforcement Learning?
Reinforcement Learning (RL) is a subtype of Machine Learning focused on an "artificial agent" system.
This system is trained and learns based on how to get desired responses through trial and error.
As a result, AI models can improve behavior over time all by themselves.
What's more, AI models can get better with an experience-based logic.
This thought process makes Reinforcement Learning quite popular in the gaming industry.
A perfect instance of RL in action is DeepMind’s AlphaGo, the first AI program to defeat the world’s Go champion.
Another instance of thriving RL was when OpenAI Five defeated Dota2 players.
These victories prove the power of Reinforcement Learning in decision-making and solving complex tasks.
Reinforcement Learning has also been widely used in the robotics field.
Software Engineers have used it to train general-purpose robots on inspecting, delivery and maintenance.
A great example is Google's MT Opt, which introduced scalable data-collection mechanisms for goal-conditioned RL.
What is Deep Reinforcement Learning?
To sum it up, Deep Reinforcement Learning blends RL with Neural Network and Deep Learning to solve complex problems.
With Artificial Neural Networks, it's possible to handle much larger datasets. That's why it's called "Deep" Reinforcement Learning (Deep RL)!
Deep RL made great contributions to Natural Language Processing (NLP), Computer Vision and Medical Diagnosis.
Similar to traditional Reinforcement Learning, Deep RL is also popular in fields like gaming, robotics, and image processing.
It has also become widely used in the development of self-driving cars.
How Does Reinforcement Learning Work?
The dynamic environment of Reinforcement Learning has three core edges: agents, goals and rewards.
RL agents can be seen as the main players who navigate the learning processes.
Likewise, goals give agents direction and shape its actions.
The last element, reward functions, are what consolidates RL's trial-and-error logic.
As the agent interacts with a simulated environment, it will perform tasks until it gets to the defined goal.
Every time it reaches the goal, it gets a reward. But what is a reward function?
A common analogy would be to think of getting a high-five or a cookie when completing a task.
RL agents receives positive signals to let them know they did what they were supposed to.
In an adventure video game, the outcome could be getting to the end of a maze.
Contrariwise, n a driving video game, the goal could be reaching a destination.
The agent must maximize the number of future rewards it gets over time, also known as cumulative rewards.
Yet, the agent won't know if it's taking the right actions until getting—or not— the reward.
This makes it likely for it to make quite a few mistakes during the process.
There's one more key concept in the agent’s environment, and it’s the state.
The state shows agents' current position, bringing information from previous actions to help shape current ones.
As a result, a hard edge of RL is teaching the system the actions that led to the desired result.
Known as credit assignment, this aspect is key for future actions to get the maximum reward.
Challenges of Reinforcement Learning
The main challenges of RL revolve around "credit assignment."
The goal will always be to get the agent to take better and better actions.
To do so, it must be able to complete tasks more quickly and maximize the number of rewards over time.
But, to reach that point, engineers must be able to filter out the wrong decisions it took to reach its goal.
It's also important to highlight the decisions that led the agent to achieve its goal.
Some experts refer to these decisions as the "credit assignment problem."
Some techniques use penalties apart from rewards every time an undesired outcome occurs.
In this context, negative outcomes can happen due to bad actions taken by the agent.
However, getting the agent to understand that only specific actions were negative is extremely complex.
Think of a game where a car is moving steadily to a destination. Just before it reaches it, it takes the wrong turn and crashes.
Credit assignment should preserve all of the good movements the car made before filtering out the ones that led it to crash.
Yet, this process is not as "independent" as you may think.
Engineers must assign specific values to every possible move the agent can make.
Moreover, these values are defined based on risks and rewards related to every possible movement.
Teams here use policy methods to shape decisions for the agent to take actions based on risk-reward relations.
Reinforcement Learning Algorithms
Reinforcement Learning Policy Iteration
Policy Iteration (PI) is a refinement algorithm that helps find the optimal policy for a given agent.
It first assesses how good the current policy is.
To do so, it focuses on the value functions that indicate how well the agent can do.
The algorithm then updates and improves the policy based on its findings.
A RL-PI refines the plan or rules the agent follows to get better results in every state.
Reinforcement Learning Value Iteration
Similar to Policy Iteration, Value Iteration (VI) aims to find optimal policies for an agent.
Yet, it leverages dynamic programming principles to maximize the cumulative reward.
This process is achieved by breaking a complex problem into smaller problems.
Value Iteration also evaluates the current value function and improves it.
RL-VI is often seen as more efficient than Policy Iteration since it does both tasks in one take.
Value Iteration is known for using the Bellman equation to update the value function.
Reinforcement Learning Q-Learning
The Q-Learning algorithm guides the agent by focusing on Quality Values (Q-values).
These represent the expected future rewards based on each action in a given state.
Mathematically, the Q-learning algorithm updates the Q-values (action and state).
In this context, it takes them as input based on observed rewards.
Think of the action as every available decision the agent can make in a given state.
The goal is to get the agent to learn the best actions it can take to maximize the cumulative reward.
That’s what makes Q-learning very useful when agents have to explore a large space with no prior knowledge.
An example could be a robot learning optimal actions in unfamiliar environments or unmanageable spaces.
Reinforcement Learning Deep Q-Network
Often seen as an extension of Q-Learning, Deep Q-Networks use Neural Networks to reach the Q-value.
Q-learning stores Q-values in tables that work as a grid of the available actions the agent can take in a given state.
Think of it as a robot moving across cells in a grid, each cell representing a state-value pair.
Deep Q-learning replaces that Q-table with a NN to handle a wider range of high-dimensional spaces.
Yet, this procedure may not be ideal if the space is too large and complex.
Deep Q-Network is great for more advanced tasks, such as complex gaming environments.
Picture the game Atari 2600 Breakout.
A "simple" Q-table would need an entry for every possible combination of paddle, ball, and brick positions.
That would be simply impractical!
Reinforcement Learning Policy Gradients
All of the prior algorithms we've mentioned so far are value-based.
They estimate the value function and then they optimize the policy.
However, Policy Gradients (PG) directly optimize policies without explicitly estimating value functions.
This scope considerably simplifies the algorithm design of RL-PG.
Like to Deep Q-Network, Policy Gradients use NN to handle high-dimensional spaces effectively.
This makes it ideal for complex scenarios where the agent has many available options.
That's why they work great in complex video games like Chess and Go.
Why is Reinforcement Learning Important?
Reinforcement Learning is a powerful force for optimizing decision-making for users and businesses alike.
As mentioned, it’s used to build AI agents in fields like robotics and video games, yet its benefits go far beyond!
RL also gained popularity in Fintech, Healthcare, Autonomous Vehicles and even the development of Smart Cities.
Widespread success cases of RL includes the Atlas Robot from Boston Dynamics.
There's also an OpenAI's robot hand that can solve the Rubik's cube.
Conclusion
THere's no doubt that Reinforcement Learning is a promising concept.
Its logic and structures allow it to build state-of-the-art models for a wide array of uses.
In fact, some of the largest companies in the world are already using it to deliver disruptive products.
We cannot wait to see what new improvements it will bring us in the future!
If you also want to be at the forefront of the future, get in touch!
