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What is Reinforcement Learning: Introduction, Definition, and Techniques

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It occurred to me today morning, on my way to the office, when I was boarding the 8.45 am metro. I was seven minutes late and missed the earlier one. But yesterday I was on time. Why is it that I am not on time every day? I wondered. I felt like punishing myself for being late today, just as I rewarded myself with a Bourneville yesterday, for being on time. But yes, I did learn a lesson today; to be more organized. I will have to check my calendar for meetings, beforehand, instead of checking the day planner in the morning, keep the car keys in the key holder, set the alarm 10 minutes earlier, and … the list goes on. Well, Reinforcement Learning is just like that. Learning by rewards and penalty. This way one may learn from each failure or success and study behavioural patterns of the environment and prepare himself better for next time.

Reinforcement Learning (RL) is a fast-growing concept and producing a wide variety of learning algorithms for different applications. I will start with Reinforcement Learning introduction and then move on to Deep Reinforcement Learning, Reinforcement Learning in Artificial Intelligence, and career opportunities.

Reinforcement Learning

In this article, I aim to discuss:

  • What is Reinforcement Learning
  • Approaches to Reinforcement theory of Learning
  • Reinforcement Learning in Artificial Intelligence
  • Reinforcement Learning algorithms
  • Reinforcement Learning techniques and applications
  • Newer reinforcement learning techniques
  • Reinforcement Learning tutorial

By the end of this article, you will have a thorough understanding of Reinforcement Learning and its practical implementation.

What is Reinforcement Learning?

Reinforcement Learning is a mathematical framework for developing computer agents that can learn an optimal behaviour by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.

Reinforcement Learning examples include DeepMind and the Deep Q learning architecture in 2014, beating the champion of the game of Go with AlphaGo in 2016, OpenAI and the PPO in 2017.

Reinforcement Learning: An Introduction

Reinforcement Learning is an approach to automating goal-oriented learning and decision-making. This approach is meant for solving problems in which an agent interacts with an environment and receives reward signal at the successful completion of every step. RL algorithms aim to find a policy, which means a mapping from state to action, which maximizes the expected cumulative reward (value function) under that policy.

Reinforcement Learning Definition

Reinforcement Learning refers to goal-oriented algorithms, which aim at learning ways to attain a complex object or maximize along a dimension over several steps. Most of the learning happens through the multiple steps taken to solve the problem. The objective is to learn by Reinforcement Learning examples.

You may start with a blank slate, and then strive to reach the goal, under the right conditions. Just like a whiz kid tries out different ways to achieve his goals, through trial and error, learning from his mistakes, Reinforcement Learning is attaining success through a series of steps. These steps may vary widely from problem to problem. But the result is the same; making the right decisions and getting rewarded when you make the right ones – this is reinforcement.

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Reinforcement Learning in Artificial Intelligence

Reinforcement Learning, in the context of AI, is a type of dynamic programming that teaches you algorithms using a system of reward and punishment. Deep Reinforcement Learning (DRL) is a fast evolving subdivision of Artificial Intelligence that aims at solving many of our problems. On the one hand, it mirrors human learning by exploring and receiving feedback from environments, much in the lines of artificial general intelligence, or AGI, Reinforcement Learning has also demonstrated success of dramatic game changes, where bipedal agents learn to walk in a simulation.

While supervised Machine Learning trains models based on known answers, Reinforcement Learning, and researchers train the model through an agent, which interacts with the environment. The agent is rewarded every time its actions produce positive results.

Reinforcement Learning though has its roots in reinforcement theories of animal learning has evolved as a solution for the betterment of mankind. Personalization Travel Support System, for example, is a solution that applies the reinforcement learning to analyze and learn customer behaviors and list out the products that the customers wish to buy. If the system selects the right item that the customer wishes to buy then it errands rewards, and gets a penalty, if it fails to do so. In this way, the system learns about user behavior and preferences, which help it redefine its actions, for particular users.

Reinforcement Learning

Reinforcement Learning in AI

Reinforcement Learning Algorithms

Reinforcement Learning algorithms are widely used in gaming applications and activities that require human support or assistance. Usually, an RL setup is composed of two components, an agent, and an environment. The environment refers to the object that the agent is acting on, while the agent represents the RL algorithm. The environment starts by sending a statement to the agent, which then based on its knowledge to take an action in response to that state. After that, the environment sends a pair of next state and reward back to the agent. The agent will update its knowledge with the reward returned by the environment to evaluate its last action. The loop keeps going on until the environment sends a terminal state, which ends the episode.

Some of commonly used RL algorithms are:

  • Q-Learning: Q-Learning is an off-policy, model-free RL algorithm based on the well-known Bellman Equation:

E in the above equation refers to the expectation, while ƛ refers to the discount factor. We can re-write it in the form of Q-value:

The optimal Q-value, denoted as Q* can be expressed as:

Two value update methods that are closely related to Q-learning are Policy Iteration and Value Iteration.

  • State-Action-Reward-State-Action (SARSA):

SARSA, another popular RL algorithm, is quite similar to Q-learning. The key difference between SARSA and Q-learning is that SARSA is an on-policy algorithm. It implies that SARSA learns the Q-value based on the action performed by the current policy instead of the greedy policy.

The Sarsa algorithm is an On-Policy algorithm for TD-Learning. The primary difference between it and Q-Learning is that the maximum reward for the next state is not necessarily used for updating the Q-values. Instead, a new action, and therefore reward is selected using the same policy that determined the original action.

  • Deep Q Network (DQN):

DQN leverages a Neural Network to estimate the Q-value function. The input for the network is the current, while the output is the corresponding Q-value for each of the action.

In 2013, DeepMind applied DQN to Atari game. The input is the raw image of the current game situation. It went through several layers including convolution layer as well as a fully connected layer. The output is the Q-value for each of the actions that the agent can take.

Two essential techniques for training DQN are Experience Replay and Separate Target Network.

Reinforcement Learning

RL techniques

Reinforcement Learning Techniques and Applications

The primary goal in RL is learning how to map observations and measurements to a set of actions while trying to maximize some long-term reward. This usually involves applications where an agent interacts with an environment while trying to learn optimal sequences of decisions. In fact, many of the initial applications of RL are in areas that require automating sequential decision-making.

Several applications and products rely on RL. You will notice that the settings in these applications involve personalization, or the automation of well-defined tasks, which would benefit from sequential decision-making that RL can help automate. Here, I have included a few of these applications:

  • Robotics and Industrial Automation:

Reinforcement Learning (RL) enables a robot to autonomously discover an optimal behavior through trial-and-error interactions with its environment. In Reinforcement Learning, the agent (i.e., the designer of a control task) provides constructive feedback in terms of a scalar objective function that measures the one-step performance of the robot. This serves as a guideline for deciding the next action.

Industrial automation is another major area where Reinforcement Learning has contributed significantly. A classic example would be of Google, which has reduced energy consumption (HVAC) in its own data centers through RL technologies from DeepMind. Startups like Bonsai use RL for industrial applications.

  • Data Science and Machine Learning:

With machine learning libraries becoming more accessible, deep learning techniques are widely being used by data scientists and machine learning engineers, to help people identify and tune neural network architectures are active areas of research. Several research groups have used RL to make the process of designing neural network architectures easier. AutoML from Google, for example, uses RL to produce state-of-the-art machine-generated neural network architectures for computer vision and language modeling.

  • Education and Training:

Reinforcement Learning is already showing ripples in online tutorials and virtual classrooms. Deep Learning researchers are looking for new ways use RL and other machine learning methods in online tutoring systems and personalized learning. Reinforcement Learning tutorials will be instrumental in providing custom instruction and materials to serve the needs of individual students. RL algorithms and statistical methods may also be developed in such a way that requires less data for use in future tutoring systems.

  • Healthcare:

Healthcare is another area where Reinforcement Learning is fast creating impressions. The RL setup of an agent may interact with an environment receiving feedback based on actions taken. Several RL applications in health care mostly pertain to finding optimal treatment policies. Deep learning scientists are researching on RL applications that serve the purpose of medical equipment, medication dosing, and two-stage clinical trials.

Some of the other applications of Reinforcement Learning include cross-channel marketing optimization and real-time bidding systems for online display advertising.

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Approaches to Reinforcement Theory of Learning

Reinforcement Learning has a number of approaches. Here, I have discussed three most well-known approaches: Value-based Learning, Policy-based Learning, and Model-Based Learning Approaches.

  • Value Based Learning Approach:

Value-based Learning estimates the optimal value function, which is the maximum value achievable under any policy. Storing the value function (or) policy might not be possible especially if the state-action pairs are high dimensional. Thus, function approximators like linear regression, Neural networks are used. In value-based RL, the goal is to optimize the value function V(s). The value function is a function that tells us the maximum expected future reward the agent will get in each state.

The value of each state is the total amount of the reward an agent can expect to accumulate over the future, starting in that state. Then the agent uses this value function to select which state to choose at each step. The agent decides to take up the state with the biggest value.

  • Policy-Based Learning Approach:

Policy-based Learning searches directly for the optimal policy which achieves the maximum future reward. In policy-based approach, we want to directly optimize the policy function π(s) without using a value function. The policy is what defines the agent behavior at a given time. We learn a policy function. This lets us map each state to the best corresponding action.

This approach has two types of policy:

  • Deterministic: a policy at a given state will always return the same action.
  • Stochastic: output a distribution probability over actions.
  • Model-Based Learning Approach:

In Model-based RL, the environment is treated as a model for learning. This means a model of the environmental behavior is created. This is a great approach until you discover that each environment will need a different model representation.

Reinforcement Learning Tutorial

If you are looking for a beginner’s or advanced level course in Reinforcement Learning, make sure that apart from a basic introduction, it includes a deep delving analysis of RL with an emphasis upon Q-Learning, Deep Q-Learning, and advanced concepts into Policy Gradients with Doom and Cartpole. You should choose a Reinforcement Learning tutorial that teaches you to create a framework and steps for formulating a Reinforcement Learning problem and implementation of Reinforcement Learning. You should also know about recent RL advancements. I suggest you visit Reinforcement Learning communities or communities, where the data science experts, professionals, and students share problems, discuss solutions, and answers to RL-related questions.

Machine learning or Reinforcement Learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors.

 Reinforcement Learning

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