Reinforcement Learning in Cognitive Science
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Questions and Answers

What is the purpose of the embedding function in the context of video games?

  • To calculate the player's score
  • To simulate the player's actions
  • To identify relevant state features (correct)
  • To create visual graphics for gameplay

    • How does the value function estimate the outcome of an action?

  • By providing a score based on current state and action (correct)
  • By running multiple simulations
  • By evaluating the graphics of the game environment
  • By analyzing past player performances

    • What uncertainty does a model face when predicting future states in a game?

  • Uncertainty in the game's storyline
  • Uncertainty in the game's graphics
  • Uncertainty about other players' actions (correct)
  • Uncertainty about the game controls

    • What does the dynamics function in a game model try to learn?

    <p>How actions impact the game's state</p> Signup and view all the answers

    What role does uncertainty play in decision making within a game model?

    <p>It complicates the modeling of player actions</p> Signup and view all the answers

    In the context of video game states, which aspect is emphasized as not being relevant?

    <p>Cloud positions in the sky</p> Signup and view all the answers

    What does the model not do when estimating the value of an action?

    <p>Run simulations of all possible game states</p> Signup and view all the answers

    Which of the following best describes the current state in a game according to the model?

    <p>A dynamic reflection of relevant features</p> Signup and view all the answers

    What is a critical aspect of decision-making in reinforcement learning?

    <p>Planning sequences of actions for greater rewards</p> Signup and view all the answers

    What might happen if a player chooses to jump for an immediate reward in a game scenario?

    <p>They could face negative consequences, such as losing a life</p> Signup and view all the answers

    What should a player consider when planning their moves in a game?

    <p>The current state of the world and future consequences</p> Signup and view all the answers

    In the context of reinforcement learning, what is often the ultimate goal?

    <p>To collect as much reward as possible</p> Signup and view all the answers

    Which of the following actions may indicate poor decision-making in a game context?

    <p>Jumping for immediate rewards without a plan</p> Signup and view all the answers

    What is typically the primary method of evaluating decisions in reinforcement learning?

    <p>Immediate reward gained from an action</p> Signup and view all the answers

    What role does the current state of the world play in decision-making processes?

    <p>It dictates the possible rewards available</p> Signup and view all the answers

    What might be a consequence of focusing solely on immediate rewards during gameplay?

    <p>Players can miss out on larger rewards later</p> Signup and view all the answers

    What characterizes the decision-making process of expert players in high-pressure situations?

    <p>They often rely on automatic responses from past experiences.</p> Signup and view all the answers

    Which statement best describes how experts, such as musicians or chess players, perform tasks?

    <p>They execute pre-planned sequences with little conscious thought.</p> Signup and view all the answers

    In what scenario might a skilled chess player be less active in decision-making?

    <p>When they have practiced a particular game state repeatedly.</p> Signup and view all the answers

    What advantage does an expert player have when recognizing familiar situations?

    <p>They can quickly estimate the quality of an action.</p> Signup and view all the answers

    Why might an expert chess player's choices seem automatic?

    <p>They have seen similar situations multiple times.</p> Signup and view all the answers

    What is a common outcome for experts who perform a sequence of actions without conscious thought?

    <p>They can execute tasks quickly and accurately.</p> Signup and view all the answers

    What aspect of expertise allows players to perform well under pressure?

    <p>A vast repository of memorized sequences.</p> Signup and view all the answers

    What do expert players rely on to handle familiar game states?

    <p>Pre-planned sequences established from practice.</p> Signup and view all the answers

    What does the term 'chunking' refer to in the context of expertise?

    <p>Recognizing patterns of meaningful configurations</p> Signup and view all the answers

    How does expertise influence attention in a complex environment?

    <p>It helps individuals filter and prioritize relevant information</p> Signup and view all the answers

    What can make it challenging for a novice driver to manage the driving environment?

    <p>The overwhelming number of stimuli in the environment</p> Signup and view all the answers

    What is a key feature of recognition in expertise according to the content?

    <p>Expertise allows for the recognition of abstract patterns and templates</p> Signup and view all the answers

    What is the impact of familiarity with a video game on the ability to recognize chunks?

    <p>Familiarity significantly enhances the ability to identify meaningful chunks</p> Signup and view all the answers

    What does the content suggest is a common issue for beginners in activities like driving?

    <p>Difficulty in managing attention to relevant signals</p> Signup and view all the answers

    What aspect of expertise helps define what to pay attention to in a complex environment?

    <p>The development of templates for interpreting relevant information</p> Signup and view all the answers

    What does the term 'recognition' imply in the context of expertise with vaguely familiar pieces?

    <p>The ability to identify pieces based on prior awareness or vague familiarity</p> Signup and view all the answers

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

    <p>A model-free learner relies on past rewards, while a model-based learner considers the overall structure of the environment.</p> Signup and view all the answers

    If a model-free learner had a positive experience with the 70s gold station, what would they likely do the next day?

    <p>Turn on the 70s gold station again.</p> Signup and view all the answers

    Why might a model-based learner choose the Today's hits station instead of the 70s gold station despite a past positive experience?

    <p>They only want to hear Taylor Swift and know it's unlikely on the 70s station.</p> Signup and view all the answers

    What might a scenario represent where a past action is not the best decision to make currently?

    <p>Learning about a gate closure that blocks your usual route.</p> Signup and view all the answers

    What is the primary focus of a model-based learner when making a decision?

    <p>Analyzing potential outcomes based on knowledge of the environment.</p> Signup and view all the answers

    If someone prefers to hear pop music, which station should they typically choose based on the content?

    <p>The Today's hits station is the right choice for them.</p> Signup and view all the answers

    What outcome might a person anticipate when choosing a station based on their goal?

    <p>The song selection might be unexpected but still enjoyable.</p> Signup and view all the answers

    When considering music stations, what type of learning strategy allows for adjustment based on unpredictable outcomes?

    <p>Model-based learning, which takes into account future expectations.</p> Signup and view all the answers

    What is the primary goal in a game of tic-tac-toe?

    <p>To get three of your pieces in a row</p> Signup and view all the answers

    How can a player determine whose turn it is in tic-tac-toe?

    <p>By checking the layout of X's and O's</p> Signup and view all the answers

    What does the Q value represent in the context of tic-tac-toe?

    <p>The average outcome of an action in a specific game state</p> Signup and view all the answers

    What is the role of previous games in computing the Q value?

    <p>They offer historical data for assessing outcomes</p> Signup and view all the answers

    When is it possible to achieve a winning state in tic-tac-toe?

    <p>In a sequence of moves following the first turn</p> Signup and view all the answers

    What type of strategy is not considered when calculating the Q value?

    <p>Multi-step strategy</p> Signup and view all the answers

    In what scenario can someone compute Q values without understanding the game rules?

    <p>By observing others play the game</p> Signup and view all the answers

    What does placing an X in the top left corner represent in the context of tic-tac-toe?

    <p>The first possible action on an empty board</p> Signup and view all the answers

    Study Notes

    Reinforcement Learning

    • Reinforcement learning is a framework for how people make multi-step plans for the future.
    • Unsupervised learning has no supervision, meaning the agent isn't told its goals for perceptions or actions.
    • Supervised learning involves a feedback signal, where a decision's correctness is immediately known.
    • Reinforcement learning often doesn't provide immediate feedback for every action.

    Cognitive Science

    • Cognitive science, studies the ways people make decisions and act within the physical world.

    Problem Solving

    • Problem solving in cognitive science refers to an agent trying to get a state of the world to a desired goal state.
    • Rewards are not always immediately given, occurring later in the chain of actions.
    • Planning involves a sequence of actions to reach a goal.
    • There are model-free and model-based strategies:
      • Model-free strategy uses previous experience.
      • Model-based strategy creates a plan based on a model of the situation, including predictions.

    Reinforcement Learning Strategies

    • Model-free strategy relies on past experience to determine the next step, often good for repetitive tasks.
    • Model-based strategy relies on a model of the world, constructing a plan to reach the goal.

    Games Examples

    • Tic-tac-toe:

      • Winning (or losing) is the reward.
      • States are represented by current board positions, player turns (or available moves).

    • Q-learning:

      • The goal is to determine the 'quality' ( Q value) of each action, given a state.
      • Q values are calculated based on past experience.
      • The strategy chooses the action with the highest estimated future reward, not just immediate reward.

    • Atari games:

      • Complex games with randomness in the rules, and high number of states.
      • The challenge is the complexity of the game state itself and randomness of potential next outcomes, making modeling difficult for a simple model. 

    • Chess and Go:

      • Expert players use a combination of model-free and model-based approaches

    • Problems:

      • State spaces can be extremely large (infinite or very high number of possible future states).
      • Delayed reward: Good choices are often good many steps in the future, not immediately. 

    • Model Building:

      • Helps estimate the future state, considering what will happen.
      • Allows planning actions well in advance - like a plan.

    • Learning methods in games:

      • Learning methods are used to find a optimal strategy. 
      • Experience is used to build a model of game behaviours and estimate their quality, to quickly find optimal moves. 

    • Expert players rely on patterns and learned 'chunks' of game data to determine good moves in complex scenarios.

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    Introduction to Cognitive Science PDF

    Description

    Explore the concepts of reinforcement learning within the context of cognitive science. This quiz delves into decision-making processes, problem-solving strategies, and the distinction between supervised and unsupervised learning. Test your understanding of how these elements interact in planning and reaching goals.

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