Reinforcement Learning (RL) is at the forefront of all transformations in the domain of Artificial Intelligence (AI). Its environment-based, trial-and-error learning method is a significant upshot in the world of data analytics. As the AI software market reaches $62.5 billion in 2022, let’s understand what is reinforcement learning and how it dictates the trends of technological progress, from self-driving cars to industrial automation. 

Reinforcement learning is a feedback-based training mechanism for Machine Learning (ML) models. It involves an AI agent participating in an unknown environment to achieve some predetermined goals without human intervention. This allows it to learn the rules of the complex environment. Primarily, through trial and error based on which action gets rewarded or penalized. Simply put, reinforcement learning forces an AI agent to discover the optimal chain of decisions. It define ‘correct behavior’ within a model environment.

How Does Reinforcement Learning Work

The five primary elements of an RL system are a learning AI agent, an interactive environment, a reward policy, a reward signal, and a value function. The reward signal determines the immediate reward for making a decision in a specific state. The value function projects the probability of future rewards for continuing from that state of correctness. RL algorithms determine the correct action policy for optimal functioning. Mainly by maximizing the average of the reward signal and value function at every stage of the problem.

Types of Reinforcement Learning

To understand what is reinforcement learning, you have to know its two main types of processes: (a) positive reinforcement, and (b) negative reinforcement. Human intervention in RL is limited to refining or altering the rewards at every stage of decision-making. Positive reinforcement is when the model designer introduces a specific stimulus within the environment. This increases the probability of correct behavior. Negative reinforcement is the removal of certain undesirable conditions from the environment that boosts the recurrence of positive decisions.

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Benefits of Reinforcement Learning

One intrinsic aspect of RL is its ability to run multiple gameplays (possibilities) of a given hypothesis, and simultaneously analyze the experiences. Its benefits include:

1. A holistic approach to a problem: Unlike traditional ML models, RL algorithms can work towards a long-term vision without dividing the problem into simpler sub-categories.
2. Creates training data in real-time: As the agent learns from its own experiences, it doesn’t require a supervised data-feeding mechanism to function.
3. Inherently adaptive: The trial-and-error learning mechanism is designed for uncertain, complex environments.

Reinforcement Learning Algorithms

To fully grasp what is reinforcement learning, its two implementation methods hold crucial importance:

Model-free learning: There are two main types of model-free learning—value-based and policy-based. Value-based algorithms, through successive trials and errors, determine the highest reward probabilities for existing in a state. The higher the value function, the more optimal the agent’s plan of action. 

In policy-based methods, a strictly defined policy is kept in the algorithm’s memory while learning. Policies are strategies to map actions with situations. They guide the reward mechanism of the algorithm at every stage. Policy-based methods can be of two types: deterministic and stochastic.

Model-based learning: A virtual model enables the algorithm to sample states and predict the likelihood of reward or punishment for every action it takes. Updating the model helps accommodate changes in learning.

Reinforcement Learning Applications

What is reinforcement learning’s role in real-life scenarios when dealing with unpredictive events? Here are a few examples:

• Industry-maintenance automation optimizing production chains
• Reducing energy consumption in data centers
• Self-driving cars helping evolve the automotive industry
• Advanced robotics and building general-purpose robots
• Autonomous trading decisions on optimal stock holding and selling 
• Discovering Dynamic Treatment Regimes (DTRs) and optimal policies for chronic patients

Challenges with Reinforcement Learning

Understanding what is reinforcement learning is incomplete without understanding its challenges.  

1. Time-consuming: It takes a significant amount of time for the agent to maximize performance efficiency solely from experience.
2. Reproducibility crisis: Insufficient knowledge and communication about algorithmic success in simulated environments make it very difficult to replicate or improve RL models.
3. Lack of functional understanding: Once the RL model has acquired the most efficient learning policy, the reasons behind its decisions may not always be transparent to the external observer. This limits the model’s learning curve and the mutual trust between the observer and AI agent.

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Reinforcement Learning Example

RL algorithms digitally encode complex behaviors. For example, in relation to self-driven cars made for daily travel, the algorithm will take into consideration safety, minimal ride time, pollution control, and passenger comfort. However, with a change in environment such as RL models for racing cars, the denominators focus more on speed than comfort. As it is humanly impossible to input all the specific changes in diverse environments, RL algorithms master the processes through trial and error.

What is Q-Learning Reinforcement Learning?

Q-Learning is a value-based RL method that gets its name from utilizing action values that are technically called Q-values. Q-values define the current state and action of the agent to continually determine its best course of action.  

What is the Markov Decision Process in Reinforcement Learning?

RL models formalization uses MDP. In this, the current action of an agent influences the environment, which, in turn, generates a new state for the agent. MDP maps this state-action pair against time to best sequence a decision-making process.

Markov Decision Process vs Q-Learning

Q-Learning enhances the function of the Markov Decision Process. For MDP to work, it requires two properly defined functions–state transition and reward function. Q-Learning is used precisely when MDPs have undeterminable transition and reward functions. It perfectly estimates the long-term rewards by exploring all combinations of specific state-action pairs.

As we move toward a digitally automated world, the limitations of RL will eventually fade away and its benefits will be further enhanced. To stay abreast with this inevitable future, you can further deep-dive into what is reinforcement learning, as well as explore the latest developments in the field of deep reinforcement learning, by signing up for Emeritus’ online courses on AI and machine learning.

By Bishwadeep Mitra

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