Deep Reinforcement Learning: What it is and How it Functions

Sequential decision-making is one of the key aspects of Machine Learning (ML) that facilitates automated driving, robotics, and health-care management. One of the best techniques for such kind of decision-making is reinforcement learning. However, it cannot efficiently handle data with multiple images or figures. Therefore, ML engineers and scientists have combined reinforcement learning and deep learning to formulate deep reinforcement learning to solve complex problems. This blog discusses what deep reinforcement learning is and how it differs from reinforcement learning algorithms.

What is the Difference Between Reinforcement Learning and Deep Reinforcement Learning?

Before understanding what deep reinforcement learning is, let’s quickly go through the concepts of reinforcement learning and deep learning. Reinforcement learning works similarly to a human brain. Like an infant learns how to function through its environment, in reinforcement learning, an agent (an algorithm, computer program or software) learns from its environment through trial and error and receives feedback in the form of punishment or rewards. Therefore, the algorithm does not need a sample data set to learn.

The deep learning technique is another subset of machine learning in which the algorithm interprets data in a manner similar to a human by using artificial neural networks. Remote switches, virtual assistants, voice recognition, automated cars, and personalized e-commerce recommendations are some key examples of deep learning.

Deep reinforcement learning, an advanced machine learning technique, blends the features of reinforcement learning and the deep learning technique. Therefore, ML specialists use it for complex problem-solving and automated decision-making. Similar to reinforcement learning, there are two key components in deep reinforcement learning—an agent and an environment.

The agent interacts with the environment at different time intervals. During each interval, it scans the current state of the environment and applies its strategy or policy to move to the next step. 

However, the key difference between reinforcement and deep reinforcement learning is that the latter uses deep neural networks to handle high-dimensional input spaces (complex data with multiple dimensions).

Moreover, in reinforcement learning, the policy (a function that defines the agent’s behavior) or value function (estimates reward) is represented through tables or linear models. On the other hand, deep reinforcement learning represents the value functions through deep neural networks.

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What are the Key Components of Deep Reinforcement Learning?

The key components of deep reinforcement learning are:

1. Agent

The agent is an independent computer program, software, or entity that interacts with the environment, understands it, and performs an action. Based on the action, the agent receives rewards or punishments. The agent performs the functions of a decision-maker and a learner.

2. Environment

In deep reinforcement learning, the environment is the external system, task, simulation, or all the elements that an agent interacts with, such as a gaming environment.

3. Policy Function

Policy refers to a strategy or a series of actions that the agent follows in a time step after observing the environment. 

4. Value Function

This component assesses or predicts the value of being in a state and calculates the cumulative reward the agent will be able to collect if it performs a particular action. The value functions help the agent analyze the quality of its current state and action and how it can generate more rewards. Hence, it improves the decision-making process.

5. Reward Signal

The reward signal tells the agent whether its particular action is desirable or not. It acts as a feedback mechanism which helps the agent update its policy and value function after each action.

6. Memory

The memory mechanism in deep reinforcement learning stores the state and action performed by the agent. It replays the entire scenario to help professionals determine the model’s efficiency.

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How is Deep Reinforcement Learning Used in Autonomous Driving?

Machine learning algorithms in autonomous cars need to interact with their environment constantly. Moreover, specialists cannot train the algorithm for all the possible scenarios while driving. Therefore, deep reinforcement learning algorithms facilitate autonomous driving through self-learning.

The algorithm trains the agent to learn safe driving strategies to reduce collisions. The agent observes its environment and transmits information to the actor, which provides the best action. The agent then receives the action, performs it, and receives the reward depending on the action’s performance. 

Once this cycle is complete, the agent immediately moves on to the next stage and repeats the same process. Specialists review the whole chain of data, including the state, action, and reward, to evaluate whether the actions performed by the agent are appropriate or not.

Can Deep Reinforcement Learning be Applied to Natural Language Processing Tasks?

Yes, deep reinforcement learning algorithms can be applied to Natural Language Processing (NLP) tasks such as translation, voice recognition, sentiment analysis, and text summarization. In deep reinforcement learning, scientists or engineers do not provide solutions, answers, or suggestions to the algorithm. In fact, it learns on its own through a series of actions. 

Therefore, for chatbots, engineers can use deep reinforcement learning to train the agent to interact with the users based on their dialogue. Here, the rewards can be user satisfaction or task completion. Similarly, deep reinforcement learning can perform other NLP tasks like text translation, text summarization, and generating dialogues.

However, the discussion around applying deep reinforcement learning techniques for NLP tasks is still in its nascent stage despite there being several studies on the topic.

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What are Some Challenges Associated With Implementing Deep Reinforcement Learning in Real-World Scenarios?

Some of the main challenges in implementing deep reinforcement learning algorithms in real-world scenarios are: 

1. Large Amounts of Data Required for Training

A deep reinforcement learning system requires a lot of data for training and efficient performance. Sourcing and managing large amounts of data is often a significant task.

2. Exploration-Exploitation

Since the agent in deep reinforcement learning self-learns or explores to achieve rewards, it needs to exploit the system to learn new strategies and improve its performance to earn more rewards. However, excess exploitation may not always be beneficial. Therefore, it is difficult to maintain a balance between self-learning and rewards.

3. Safety and Ethics

Safety is one of the most significant concerns of deep reinforcement learning because the agent trains itself. The agent may learn unintended behaviors resulting in wrong actions. This can impact the safety of users, especially in autonomous driving cars.

4. Stochastic (Uncertain) Environments

Deep reinforcement learning models may require some stability in their environment to perform an action effectively. However, real-life situations are often uncertain, which can impact the model’s efficiency.

With the growing use of automation, especially for complicated tasks such as driving and health management, the demand for machine learning specialists and engineers will increase in the next few years. Therefore, now is the ideal time to invest in skills-based machine learning courses to learn advanced machine learning techniques and algorithms. Explore Emeritus’ artificial intelligence courses and machine learning courses today to gain advanced knowledge of machine learning techniques and advance your career.

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