Reinforcement Learning and Its Concepts

These flashcards cover key concepts from the lecture on reinforcement learning, machine learning types, and applications in dynamic environments.

What is Reinforcement Learning (RL)?

A type of machine learning where an agent learns to make decisions by taking actions to maximize cumulative reward within a specific environment.

What is the primary objective of an agent in Reinforcement Learning?

To maximize some notion of cumulative reward by taking actions within its environment.

How does the environment affect an agent in Reinforcement Learning?

The environment constrains the agent's actions, perceptions of these actions, and ultimate goals.

What is the difference between Narrow AI and General AI?

Narrow AI is confined to specific tasks; General AI can handle multiple tasks across similar environments.

In the context of Reinforcement Learning, what are feedback loops?

Feedback loops are mechanisms where the algorithm learns from results to improve future performance.

What is the challenge of applying traditional supervised learning in dynamic environments like trading?

Traditional supervised learning cannot cope with dynamically changing conditions.

What is a common scenario illustrating Immediate Reward vs Future Consequences in trading?

Selling only a portion of holdings to mitigate losses, as it considers future market conditions.

What does clustering achieve in Unsupervised Learning?

Grouping data points based on feature similarities without predefined labels.

What is a key challenge in distinguishing features within noisy machine learning environments?

Identifying accurate features can be difficult in noisy settings or with variable conditions.

How does Self-Supervised Learning differ from supervised and unsupervised learning?

Self-supervised learning allows algorithms to label data automatically without manual input.

What are parameters used for in machine learning algorithms?

Parameters are used to fine-tune the algorithm to minimize error and align predicted results with true values.

Why is historical data important in machine learning for fintech models?

Historical data provides crucial insights but may lose relevance with continuous market fluctuations.