AIcademics
Gallery
Toggle theme
Sign In
Artificial Intelligence
Unit 1
Machine Learning
Introduction to Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Model Evaluation and Validation
Unit 2
Deep Learning
Introduction to Deep Learning
Deep Neural Networks
Training Deep Learning Models
Unit 3
Learning to Rank Models (LLM)
Introduction to Learning to Rank Models
Supervised Learning for LLM
Evaluation Metrics for LLM
Learning to Rank Algorithms
Optimization Techniques for LLM
Unit 1 • Chapter 4
Reinforcement Learning
Summary
false
Concept Check
What is one of the main challenges in Reinforcement Learning?
Exploration vs Exploitation dilemma
Model complexity
Random actions selection
Immediate rewards only
What is a common method to address the Exploration vs Exploitation dilemma?
Monte Carlo method
Temporal Difference learning
Q-Learning algorithm
Epsilon-greedy strategy
In Reinforcement Learning, what is the primary goal of the agent?
Minimize exploration
Maximize cumulative reward
Solve complex problems efficiently
Improve model accuracy
Which technique involves learning the value function in Reinforcement Learning?
Temporal Difference learning
Policy gradient methods
Random exploration
Q-Learning
What is an advantage of using function approximation in Reinforcement Learning?
Handling large state spaces efficiently
Improved exploration strategy
Faster convergence rate
Deterministic policy improvement
Check Answer
Previous
Unsupervised Learning
Next
Model Evaluation and Validation