Reinforcement Learning

What is the main focus of the Crash Course AI episode discussed in the transcript?

• deep learning
• unsupervised learning
• reinforcement learning
• supervised learning

Reinforcement Learning is particularly useful for:

• teaching AI to play simple board games
• pattern recognition in large datasets
• tasks where the process is difficult to explain
• supervised learning tasks

In Reinforcement Learning, when is feedback provided to the AI?

• continuously throughout the task
• after each action
• before the task begins
• at the very end of the task

Google DeepMind used Reinforcement Learning to teach virtual AI systems to:

• play chess
• compose music
• solve math problems
• walk and jump

The 'agent' in AI terminology refers to:

• the learning algorithm
• the environment
• the teacher
• the AI system learning through actions

In Reinforcement Learning, the 'State' represents:

• the rewards collected
• the policies decided upon
• the final goal
• the current inputs to the AI

The term 'credit assignment' in Reinforcement Learning refers to:

• the cost of computational resources
• the amount of reward given
• determining which actions led to success
• assigning credit to the AI developers

The environment in which an AI agent operates could be:

• a game board
• a digital simulation
• all of the above
• a real-life kitchen

In Reinforcement Learning, 'Value' is assigned to:

• the final reward
• different states or actions
• every possible action
• the policies used by the agent

A 'Policy' in Reinforcement Learning is:

• a government regulation on AI use
• a set of rules for the environment
• a plan of action based on values
• the learning algorithm used