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Reinforcement Learning Methods

Basic Diagram of RL_030223A
[Basic Diagram of Reinforcement Learning - KDNuggets]


- Overview

Reinforcement learning (RL) is a machine learning (ML) technique that trains software to make decisions that achieve the best results. It's a sub-field of ML that allows AI-based systems to take actions in a dynamic environment.

RL is based on rewarding desired behaviors and punishing undesired ones. It's a learning paradigm that learns to optimize sequential decisions, such as daily stock replenishment decisions. 

In RL, the entity being trained, called the reinforcement learning agent, can perceive and interpret its environment, take actions, and learn through trial and error. 

 The agent is trained on real-life scenarios to make a sequence of decisions. It receives either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.

RL mimics the trial-and-error learning process that humans use to achieve their goals. RL has three types: 

  • Policy-based RL: Uses a policy or deterministic strategy that maximizes cumulative reward
  • Value-based RL: Tries to maximize an arbitrary value function
  • Model-based RL: Creates a virtual model for a certain environment and the agent learns to perform within those constraints


Please refer to the following for more information:


- The Trial-and-Error Learning Process

Reinforcement learning (RL) is a ML technique that trains software to make decisions to achieve optimal results. It is based on rewarding desired behavior and punishing undesirable behavior.

In RL, an agent learns how to behave in its environment by performing operations and viewing the results. For every good behavior, the agent will receive positive feedback, and for every bad behavior, the agent will receive negative feedback or punishment.

RL mimics the trial-and-error learning process humans use to achieve goals. For example, you can use a reward system to train your dog. When the dog behaves well, you reward it; when it does something wrong, you punish it.

Various software and machines use RL to find the best behavior or path to take in a given situation. Some examples of RL include: predictive text, text summarization, question answering, machine translation.

Some challenges and limitations of reinforcement learning include:

  • High-dimensional and continuous state and action space
  • Noisy and incomplete data
  • Dynamic and adversarial environments


- The Markov Decision Process (MDP)

Reinforcement learning (RL) is currently undergoing rapid development in both methodologies and applications. 

Although rooted in specialized algorithms developed by the computer science community, RL has grown into a field that deals with a wide range of methods for approximately solving intractable Markov decision processes, the fundamental model for sequential decision-making under uncertainty in operations research.

The Markov decision process (MDP) is a mathematical framework used for modeling decision-making problems where the outcomes are partly random and partly controllable. It's a framework that can address most RL problems.

RL is learning what to do given a situation and a set of possible actions from which to choose, in order to maximize reward. The learner, whom we call an agent, is not told what to do, he has to discover this for himself through interaction with the environment.

So RL is a set of methods that learn "how to (optimally) behave" in an environment, whereas MDP is a formal representation of such an environment.


- The Three Approaches of RL

The three approaches to RL are: value-based, policy-based, and model-based learning. Agent, State, Reward, Environment, value function model of the environment, model-based methods, are some important terms used in RL learning methods. 

The goal of RL is to choose its actions in such a way that the cumulative reward is maximized. So choosing the best reward now may not be the best decision in the long run. That is the greedy approach may not be optimal.

RL, a type of ML in which an agent takes actions in an environment designed to maximize its cumulative reward. RL is based on rewarding desired behavior or punishing undesired behavior. Instead of one input producing one output, the algorithm produces multiple outputs and is trained to choose the correct output based on certain variables. 

RL is a ML technique in which computer agents learn to perform tasks through trial and error interactions with a dynamic environment. This learning approach enables the agent to make a sequence of decisions that maximizes the reward metric for a task without human intervention and without being explicitly programmed to complete the task.


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[Lake Brienz, Switzerland]

- Components of RL

Reinforcement learning (RL) is a method in which an agent learns how to behave in an environment by performing actions and seeing the results.

RL is associated with applications where an algorithm has to make a decision and the decision bears the consequences. The goal is defined by the maximization of the expected cumulative reward. 

Based on the input data, the algorithm assumes a state where the user rewards or punishes the algorithm based on the actions taken by the algorithm. The algorithm learns from rewards/punishments and updates itself, and so on.

• State: The agent's observations of the environment after performing the action

• Action: The action the agent performs on the environment based on its observations

• Reward: The feedback an agent receives based on the actions it performs. It is rewarded if the feedback is positive, and punished if it is negative.


There is an agent and an environment. The environment gives the agent a state. The agent chooses an action and gets a reward from the environment along with a new state. This learning process continues until a goal is reached or some other condition is met.


- RL Processes

The idea behind RL is that an agent will learn from the environment by interacting with it and receiving rewards for performing actions. Three approaches to solving RL problems:

  • Value-based: Learning state or state-action value. Act by choosing the best action in the state. Exploration is necessary. 
  • Policy-based: Directly learn a stochastic policy function that maps states to actions. Follow the sampling policy. 
  • Model-based: Learning a model of the world and then using the model for planning. Models are frequently updated and replanned.


RL is a computer technique for understanding and automating goal-directed decision-making and learning. It differs from previous computational approaches in that it focuses on the agent learning directly from the surrounding environment, rather than relying on exemplary supervision or a comprehensive model of the environment. 

However, traditional ML methods will work in many situations. In business data processing and database management, purely algorithmic solutions that do not involve machine learning are often useful. 

RL processes are sometimes used to help processes that perform in different ways, such as finding ways to increase speed or efficiency. 

Neural networks are useful when machines have to process unstructured and unsorted data, or deal with various data types. 

It cannot be denied that RL in ML is a revolutionary technology. However, it is not required in all cases. Still, RL seems like the most logical approach to making machines creative - after all, exploring new, imaginative ways to accomplish tasks is what creativity is all about.


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