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Reinforcement Learning
Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an
environment to maximise cumulative rewards. The agent learns through trial and error, taking actions, and receiving
feedback in the form of rewards or penalties. The objective is to develop a policy or strategy that enables the agent to
take actions that results in the highest cumulative reward over a period of time. Reinforcement learning is often applied
in situations requiring a series of decisions, such as playing games, controlling robots, or managing financial portfolios.
Examples of reinforcement learning algorithms include Q-learning, Deep Q-Networks (DQN), policy gradients, and
actor-critic methods.
This technique is also employed in real-world scenarios, like autonomous driving, where vehicles learn to navigate by
interacting with their surroundings. In healthcare, reinforcement learning optimises treatment plans by continuously
adjusting based on patient responses. Additionally, in recommendation systems, it enhances user experience by
learning to suggest content, based on ongoing user interactions and feedback.
Input Raw Data Environment Output
Reward Best Action
Selection of
State Algorithm
Agent
Benefits of AI
Some benefits of AI are as follows:
• Increased efficiency and productivity: AI automates tasks, analyses data rapidly, and optimises processes, boosting
efficiency and productivity across various industries.
• Improved decision-making: AI processes vast amounts of data and uncovers patterns that humans might miss,
aiding in data-driven decision-making and potentially leading to better outcomes.
• Enhanced innovation and creativity: AI tools can generate new ideas, explore possibilities, and automate repetitive
tasks, allowing human resources to focus on more creative and innovative pursuits.
• Progress in science and healthcare: AI supports drug discovery, medical diagnosis, and personalised medicine,
thereby driving advancements in healthcare and scientific research.
• Fraud detection: AI can detect fraudulent activities by analysing transaction patterns and identifying anomalies.
Limitations of AI
Some limitations of AI are as follows:
• Job displacement: Automation through AI raises concerns about job loss and the necessity for workforce retraining
and upskilling.
• Ethical considerations: There are concerns about bias in AI algorithms, potential misuse for surveillance or
manipulation, and the need for ethical guidelines and regulations.
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