Reinforcement Learning Basics

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Questions and Answers

What characterizes an expert's ability in problem solving?

  • They can only work effectively with simple state spaces.
  • They identify significant state aspects without full exploration. (correct)
  • They rely solely on conscious decision-making for actions.
  • They can evaluate all possible future actions.

What does the 'embedding' function do in the context of Reinforcement Learning?

  • Models the future states based on past actions.
  • Stores all possible actions and their outcomes.
  • Extracts relevant features of the state. (correct)
  • Sets the rewards for each action taken.

In Reinforcement Learning, what distinguishes model-free strategies from model-based strategies?

  • Model-based strategies utilize past experiences without planning.
  • Model-based strategies depend solely on trial and error.
  • Model-free strategies rely on cached knowledge about actions and rewards. (correct)
  • Model-free strategies require significant future planning.

Why might rewards in Reinforcement Learning be difficult to obtain?

<p>They can be many steps away from the initial state. (D)</p> Signup and view all the answers

What is a key benefit of using cached knowledge in problem-solving?

<p>It allows for faster processing by avoiding unnecessary evaluations. (C)</p> Signup and view all the answers

What differentiates model-free learning from model-based learning?

<p>Model-free learning does not require understanding state transitions over time. (D)</p> Signup and view all the answers

In the context of Tic-Tac-Toe, what does the Q value represent?

<p>The frequency of winning from a specific action in a given state. (A)</p> Signup and view all the answers

How do model-based systems improve their decision-making over time?

<p>By predicting high-quality actions even before experiencing states. (D)</p> Signup and view all the answers

What is meant by heuristic search in decision-making?

<p>A combination of experience-based guessing and model planning. (C)</p> Signup and view all the answers

What might challenge the effectiveness of optimal decision strategies?

<p>The rarity of information updates about the world. (C)</p> Signup and view all the answers

Which statement accurately describes a characteristic of model-free systems?

<p>They are very quick in making decisions based on experiences. (D)</p> Signup and view all the answers

What is a potential benefit of model-based learning compared to model-free learning?

<p>It can consider new information to optimize current decisions. (B)</p> Signup and view all the answers

What is an example of a 'good' state in the Tic-Tac-Toe context?

<p>A state where one player has two in a row. (D)</p> Signup and view all the answers

What characterizes the process of problem-solving in cognitive science?

<p>It requires several steps to reach a goal. (C)</p> Signup and view all the answers

Which of these strategies involves using prior experience to make decisions?

<p>Model-free approach (C)</p> Signup and view all the answers

What is the primary goal of reinforcement learning algorithms?

<p>To maximize the overall sum of rewards. (D)</p> Signup and view all the answers

In Quality (Q) Learning, what does the Q of an action represent?

<p>The sum of future rewards resulting from that action. (D)</p> Signup and view all the answers

How did reinforcement learning emerge as a field of study?

<p>By unifying classical conditioning with mechanical engineering control theory. (C)</p> Signup and view all the answers

What is a key component when deciding the next action in problem-solving?

<p>Evaluating potential actions based on experiences. (B)</p> Signup and view all the answers

Which type of learning focuses on detecting patterns without a specific goal?

<p>Unsupervised learning (B)</p> Signup and view all the answers

In the context of reinforcement learning, which of the following best describes a model-based approach?

<p>Planning multi-step actions explicitly. (D)</p> Signup and view all the answers

What is a fundamental requirement for reinforcement learning algorithms to function effectively?

<p>Having a framework for evaluating the consequences of actions. (B)</p> Signup and view all the answers

What distinguishes supervised learning from unsupervised learning?

<p>Supervised learning involves being taught the correct responses. (A)</p> Signup and view all the answers

Flashcards

State Feature Extraction

The process of extracting the most important features from a state in a problem-solving context.

Value Function

A function that estimates the value of a state, often in terms of expected reward.

Dynamics Function

A function that approximates how the state of the problem changes based on actions taken.

Model-free Strategy

A strategy that learns from previous experiences to make decisions without explicitly modeling the environment.

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Model-based Strategy

A strategy that uses an explicit model of the environment to plan future actions.

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Q-value

The estimated value of taking a specific action in a given state, often based on past experiences and rewards.

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Model-free learning

A learning approach that relies solely on experience to determine the best actions, without needing to understand how actions affect the environment.

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Model-based learning

A learning approach that uses a model of the environment to predict how actions affect states and plan future actions.

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Generalization (in AI)

The ability to make good decisions even in situations that have never been encountered before.

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Heuristic search

A technique that combines model-free and model-based strategies to make optimal decisions, using experience to guide planning.

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Challenges of using optimal decision strategies

The complexity of the environment, the number of possible states and actions, and the difficulty in understanding how actions affect states.

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Unsupervised Learning

A type of machine learning where the algorithm learns patterns from data without explicit guidance or labels.

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Supervised Learning

A type of machine learning where the algorithm is trained on labeled data to predict the correct output for given inputs.

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Problem Solving

A complex mental process where the goal is to reach a desired state by performing a series of steps.

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Reinforcement Learning

A type of learning that involves making decisions in an environment to achieve a specific goal through repeated trial and error.

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Current State

The state of the world before an action is taken.

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Next State

The state of the world after an action is taken.

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Quality (Q) Value

The value assigned to a particular action in a given state based on the expected future rewards.

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Q-Learning

A method of reinforcement learning where the Q values are learned through experience and updated based on the observed rewards.

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Greedy Policy

A type of decision-making process in reinforcement learning where the agent chooses the action with the highest expected Q value.

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Exploration-Exploitation Dilemma

A type of decision-making process in reinforcement learning that considers the long-term consequences and explores different actions to maximize the total reward.

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Study Notes

Reminders

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Reinforcement Learning (RL)

  • Emerged in the 1970s from merging psychological learning theories (classical conditioning) and control theory (mechanical engineering)
  • Useful for understanding agents making repeated decisions in an environment to achieve goals
  • RL algorithms are practical for AI systems and explain human/animal behavior

Problem Solving

  • In cognitive science, "solving a problem" means identifying one or more goal/reward states and finding a sequence of steps to reach them.
  • This often involves deciding on the next action, either using prior experience or explicitly planning a multi-step plan.

Q-Learning

  • Q(uality) is the sum of future expected rewards from an action in a particular state.
  • To learn Q values, experience is crucial; assessing how actions in a state perform in the past.

Tic-Tac-Toe Example

  • Rewards: Winning, losing, or drawing
  • States: Configurations of X's and O's on the board
  • Actions: Placing an X or O in empty spaces

Chess Example

  • States: Board configurations of chess pieces
  • Reward: Winning, losing or drawing

Model-Free vs. Model-Based RL

  • Model-free: Learning Q-values directly from experience, without needing a model of the environment.
  • Model-based: Creating a model of the environment to predict the effects of actions and plan optimal sequences of actions. Model-based methods can predict quality actions even for previously unseen states.

Real-World Challenges

  • Large state and action spaces
  • Rewards may be several steps away
  • Learning Q-values/models can take many attempts

Expertise

  • Experts in a domain have simplified methods
  • Recognize important aspects of states
  • Estimate Q-values quickly without simulation
  • Rely on cached/automatic/prior action sequences

AlphaGo, AlphaZero, MuZero

  • AI programs designed for various games (Go, chess, shogi, Atari) by using techniques to rapidly determine action quality.
  • These programs use reinforcement learning by developing or fine tuning known game rules and then applying model-free and model-based ideas to improve action selection.

Summary of Reinforcement Learning

  • This framework describes different approaches to multi-step problem solving.
  • Model-free leverages cached knowledge.
  • Model-based approaches explicitly plan sequences to reach goals/rewards.

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