Expert Problem Solving and Q Learning

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Questions and Answers

What characterizes the behavior of an expert in problem-solving?

They use only a basic understanding of the problems faced.

They identify key aspects of a state without needing to evaluate all future possibilities. (correct)

They rely solely on conscious decision-making for every action.

They can estimate Q values without considering past experiences.

What is a significant challenge in learning Q values or a model of the world?

The simplicity of state interactions.

The enormous and/or continuous set of actions involved. (correct)

The need for a limited number of attempts.

The directly available rewards for each action taken.

Which function combines a model-free value estimate with model-based lookahead?

Action function

Reward function

State evaluation function

Dynamics function (correct)

What is a key difference between model-free and model-based strategies?

Model-free strategies use cached knowledge about past actions toward goals. Signup and view all the answers

What does the 'embedding' function do in the context of expert problem solving?

It extracts relevant features of the state. Signup and view all the answers

What is a characteristic of model-free learning?

It relies on pure experience to identify effective actions. Signup and view all the answers

How do model-based learners make decisions?

They simulate possible actions based on their knowledge. Signup and view all the answers

Why might it be challenging to use multiple strategies for making optimal decisions?

Many strategies require understanding complex state transitions. Signup and view all the answers

What does the Q value represent in Tic-Tac-Toe?

The probability of winning after a specific move. Signup and view all the answers

What is a key benefit of model-based systems compared to model-free ones?

They can predict action quality for unfamiliar states. Signup and view all the answers

What does heuristic search involve in the context of decision-making?

Combining experience with planning to estimate action quality. Signup and view all the answers

Which of the following describes a model-free learner's approach when selecting the '70s Gold station?

They choose the station because of familiar positive experiences. Signup and view all the answers

What advantage do model-based systems have when new information becomes available?

They can adjust their models and update plans accordingly. Signup and view all the answers

What does 'model-free' decision-making rely on?

Choosing actions based on past experiences Signup and view all the answers

Which learning strategy involves detecting patterns without specific goals?

Unsupervised learning Signup and view all the answers

What is a primary component of problem-solving in cognitive science?

Multiple steps required to reach a goal Signup and view all the answers

What is the relationship between the Quality (Q) of an action and future rewards?

Q represents the sum of future rewards from an action Signup and view all the answers

What is the main goal of reinforcement learning?

Maximize the overall sum of rewards Signup and view all the answers

How do reinforcement learning algorithms offer insight into behavior?

By applying learning theories to different fields Signup and view all the answers

Which of the following statements is accurate regarding 'model-based' actions?

They depend on evaluating the Q of actions Signup and view all the answers

In the context of reinforcement learning, what role does control theory play?

It integrates psychological learning with decision-making frameworks Signup and view all the answers

What do agents need to determine the next action in problem-solving?

Prior experience or a detailed plan Signup and view all the answers

What does reinforcement learning primarily focus on?

Repeatedly making optimal decisions in an environment Signup and view all the answers

Study Notes

Reminders

Sign in to AttendanceRadar for a quiz.
Paper #2 is due tonight (11:59 pm).
Paper #3 proposal is due November 26th; the full paper is due December 9th.
The final exam is the same format as the midterm, but only covers the second half of the semester. Students can choose between the following dates and times for the final:
- Monday, December 9th, 2:40-3:55 pm, in class (304 Barnard Hall).
- Wednesday, December 18th, 1-4 pm, in class (304 Barnard Hall).

Reinforcement Learning (RL)

RL emerged in the 1970s from the merging of psychological learning theories (classical conditioning) and control theory (from mechanical engineering).
RL is useful for modeling agents that make repeated decisions in an environment to achieve goals.
RL algorithms can be practically useful for AI systems and also serve as explanations for human/animal behavior.

Problem Solving

In cognitive science, "solving a problem" typically means finding one or more goal states that the system desires to achieve.
This usually involves multiple steps/actions to reach that goal state.
Determining the next action can be through: model-free methods (using past experience to predict optimal actions), or model-based methods (developing explicit, multi-step plans to reach the goal).

Q-Learning

The quality (Q) of an action is the sum of future rewards anticipated from taking that action in a given state (on average).
Knowing the Q-values for every action in every state allows for optimal decision-making.
Q-values can be learned through experience. Past experiences taking an action in a given state can be used to predict the outcome (reward) of taking that action again in a similar state.

Tic-Tac-Toe and Chess Examples

In Tic-Tac-Toe, the Q-value for playing X in a specific location on a board is determined by how often a win is achieved starting from that specific board state, in past plays, with that particular action.
A similar concept applies to chess, where board states and actions considered lead to estimates of Q-values.

Model-Based vs. Model-Free Learning

Model-free learning uses experience-based methods to determine optimal actions without explicit knowledge of the system's dynamics (how state changes via actions).
Model-based learning requires a model of the system's dynamics to predict how actions will affect the state before actually executing them. This makes planning and anticipating consequences easier.

Model-Based and Model-Free Examples

Model-free learning in music/audio examples may choose the same musical segment/track repeatedly if it produced a positive result in the past, since it doesn't predict the outcome of other similar musical segments/tracks.
Model-based learning might analyze similar audio/musical tracks to predict which ones will yield a more pleasurable result.

Expertise in Problem Solving

Experts in a domain are able to identify the critical features in a given state, which can be used for optimal decision-making.
They can often estimate outcomes of a state without extensive future analysis.
They can rely on prior cached or automatic actions.

AlphaGo and Other Machine Learning

AlphaGo, an AI program able to master Go, was among the first to use neural networks and tree search algorithms for multistep game strategies.
Other algorithms (AlphaZero, MuZero) expanded on this ability to learn complex games without prior knowledge, effectively generalizing to other game types.

Learning and Planning

Real-world problems can lead to complex planning requirements due to the vastness of possible state and action spaces.
Learning optimal actions requires extensive training and calculation because future rewards often depend on many prior actions.
Experts in a domain often make use of cached, or automatic, actions, prioritizing known/familiar actions efficiently over planning new actions.

Summary

Reinforcement Learning (RL) is a framework for analyzing problem-solving and multistep planning. Model-free strategies rely on prior experiences to estimate optimal actions, and model-based strategies use prior knowledge/models of the domain to plan for future actions.

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Description

This quiz explores key concepts in expert problem solving and the challenges faced in learning Q values. It highlights the differences between model-free and model-based strategies as well as the role of the 'embedding' function. Test your understanding of these advanced topics in artificial intelligence.