Human started
making machinery that can do the job for them. The
technology developed so much that it started involving
many other branches of engineering such as electronics,
robotics etc. This eventually led to much more complex
and smart machinery involving Artificial Intelligence.
Reinforcement Learning is a type of Machine Learning,
and thereby also a branch of Artificial Intelligence. It
allows machines and software agents to automatically
determine the ideal behavior within a specific context, in
order to maximize its performance. Reinforcement
Learning (RL) comes from the animal learning theory.
RL does not need prior knowledge, it can autonomously
get optional policy with the knowledge obtained by trial-
and-error and continuously interact with dynamic
environment.
As a matter of fact, Reinforcement Learning is defined by
a specific type of problem, and all its solutions are classed
as Reinforcement Learning algorithms. In the problem,
an agent is supposed decide the best action to select based
on its current state. When this step is repeated, the
problem is known as a Markov Decision Process.
A Markov Decision Process is a discrete time stochastic
control process. At each time step, the process is in some
state s, and the decision maker may choose any action
that is available in state‘s’. Markov Decision Process
provides a mathematical framework for modeling
decision-making in situations where outcomes are partly
random and partly under the control of a decision maker.
Table of Contents
I. INTRODUCTION
II. ARTIFICIAL INTELLIGENCE
A. Definition
B. Application of AI in the Business World
III. REINFORCEMENT LEARNING
A. Definition
B. Limitations
C. Applications
D. Examples
IV. CONCLUSION
Research Objectives and Core Themes
This paper aims to provide a comprehensive review of Reinforcement Learning (RL) as a significant branch of Artificial Intelligence, exploring its theoretical foundations, operational mechanics, and its practical application across various domains. It examines how RL enables autonomous agents to optimize behavior through environmental interaction and trial-and-error learning.
- The historical evolution and fundamental definitions of Artificial Intelligence.
- The operational role and significance of Reinforcement Learning in modern technology.
- Challenges associated with computational memory, state estimation, and perceptual aliasing.
- Real-world applications of RL, including robotics, scheduling, and game playing.
- Comparative analysis of different learning methodologies within AI.
Excerpt from the Publication
D. Examples
A good way to understand Reinforcement Learning is to consider some of the examples and possible applications that have guided its development.
• A master chess player makes a move. The choice is informed both by planning (anticipating possible replies and counter replies) and by immediate, intuitive judgments of the desirability of particular positions and moves.
• An adaptive controller adjusts parameters of a petroleum refinery's operation in real time. The controller optimizes the yield/cost/quality trade-off on the basis of specified marginal costs without sticking strictly to the set points originally suggested by engineers.
• A gazelle calf struggles to its feet minutes after being born. Half an hour later it is running at 20 miles per hour.
• A mobile robot decides whether it should enter a new room in search of more trash to collect or start trying to find its way back to its battery recharging station. It makes its decision based on how quickly and easily it has been able to find the recharger in the past.
• Ramon prepares his breakfast. Closely examined, even this apparently mundane activity reveals a complex web of conditional behavior and interlocking goal – sub-goal relationships: walking to the cupboard, opening it, selecting a cereal box, then reaching for, grasping, and retrieving the box. Other complex, tuned, interactive sequences of behavior are required to obtain a bowl, spoon, and milk jug. Each step involves a series of eye movements to obtain information and to guide reaching and locomotion. Rapid judgments are continually made about how to carry the objects or whether it is better to ferry some of them to the dining table before obtaining others. Each step is guided by goals, such as grasping a spoon or getting to the refrigerator, and is in service of other goals, such as having the spoon to eat with once the cereal is prepared and ultimately obtaining nourishment.
Summary of Chapters
I. INTRODUCTION: Provides an overview of the history of computing and the emergence of Artificial Intelligence as a discipline focused on intelligent agent behavior.
II. ARTIFICIAL INTELLIGENCE: Defines the field of AI, its sub-disciplines such as robotics and neural networks, and its growing integration into business and professional sectors.
III. REINFORCEMENT LEARNING: Details the theoretical framework of RL, its reliance on reward-based feedback loops, and its challenges in practical deployment like memory constraints.
IV. CONCLUSION: Reflects on the future trajectory of AI, emphasizing the potential of super-computers and the evolving nature of machine learning technologies.
Keywords
Artificial Intelligence, Reinforcement Learning, Machine Learning, Markov Decision Process, Autonomous Agents, Robotics, Neural Networks, Intelligent Systems, Reward Feedback, Optimization, Real-time Control, Strategic Planning, Algorithmic Learning, Computer Science, Automation.
Frequently Asked Questions
What is the primary focus of this research paper?
The paper focuses on reviewing the current state and utility of Reinforcement Learning (RL) as a branch of Artificial Intelligence, highlighting how it differs from other machine learning techniques through autonomous, reward-based decision making.
What are the core thematic areas discussed in the work?
The paper covers the definition of AI, the specific mechanics of RL, the challenges researchers face (such as memory costs), and the wide range of real-world applications including industrial robotics and logistics.
What is the primary research question or goal?
The primary goal is to examine how Reinforcement Learning allows software agents to automatically determine ideal behaviors in dynamic environments to maximize performance outcomes.
Which scientific methods are employed?
The paper utilizes a literature-based review and theoretical analysis of Markov Decision Processes (MDP) and existing Reinforcement Learning frameworks.
What topics are covered in the main body of the paper?
The main body treats the definition of AI, the application of AI in business, the specific components of RL, the limitations of current RL algorithms, and various illustrative examples of RL in action.
Which keywords define this paper?
The work is defined by terms such as Artificial Intelligence, Reinforcement Learning, Machine Learning, Markov Decision Process, and Autonomous Agents.
How does the author define the relationship between AI and Reinforcement Learning?
The author identifies Reinforcement Learning as a specific type of machine learning and a sub-branch of Artificial Intelligence, emphasizing that it is defined by the problem it solves rather than just the learning method used.
Why is the Markov Decision Process (MDP) relevant to this research?
The MDP provides the essential mathematical framework for modeling the decision-making process in RL, specifically when outcomes involve a combination of randomness and control by the agent.
What are the main limitations identified in Reinforcement Learning research?
The main limitations include the high memory costs of storing values for complex states and the difficulty of accurately determining the current state due to limited perception, a problem often referred to as perceptual aliasing.
- Citar trabajo
- Mehdi Samieiyeganeh (Autor), Parisa Bahraminikoo (Autor), G. Praveen Babu (Autor), 2012, Review on use of Reinforcement Learning in Artificial Intelligence, Múnich, GRIN Verlag, https://www.grin.com/document/195798
