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Questions and Answers
What is the primary goal of a finite-horizon model?
What is the primary goal of a finite-horizon model?
In an infinite-horizon model, rewards further in the future are completely ignored.
In an infinite-horizon model, rewards further in the future are completely ignored.
False
What does the Bellman’s equation help to determine?
What does the Bellman’s equation help to determine?
The optimal policy π* and the value of states or state-action pairs.
In the context of cumulative reward, the function representing the policy is denoted as π: S → _____ .
In the context of cumulative reward, the function representing the policy is denoted as π: S → _____ .
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What is the effect of the discount factor in an infinite-horizon model?
What is the effect of the discount factor in an infinite-horizon model?
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Match the components with their descriptions:
Match the components with their descriptions:
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The agent's behavior defined by policy π is independent of the available actions.
The agent's behavior defined by policy π is independent of the available actions.
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What determines how good it is for the agent to perform action at in state s?
What determines how good it is for the agent to perform action at in state s?
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What is the purpose of Bellman's equation in reinforcement learning?
What is the purpose of Bellman's equation in reinforcement learning?
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Model-Based Learning requires exploration of the environment to find the optimal policy.
Model-Based Learning requires exploration of the environment to find the optimal policy.
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What iterative algorithm is used to find the optimal policy in the value iteration process?
What iterative algorithm is used to find the optimal policy in the value iteration process?
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The optimal policy is obtained by choosing the action that maximizes the value in the ______ state.
The optimal policy is obtained by choosing the action that maximizes the value in the ______ state.
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Match the following terms to their descriptions:
Match the following terms to their descriptions:
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Which of the following is true about the value convergence in value iteration?
Which of the following is true about the value convergence in value iteration?
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In Policy Iteration, the policy is updated indirectly through the values.
In Policy Iteration, the policy is updated indirectly through the values.
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What condition is used to determine when values have converged in value iteration?
What condition is used to determine when values have converged in value iteration?
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What is the primary aspect of policy iteration in reinforcement learning?
What is the primary aspect of policy iteration in reinforcement learning?
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Exploration strategies aim to find the optimal policy by only exploiting known actions.
Exploration strategies aim to find the optimal policy by only exploiting known actions.
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What is the significance of the ε parameter in the ε-greedy search strategy?
What is the significance of the ε parameter in the ε-greedy search strategy?
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In model-free learning, the model of the environment is _____ and requires exploration.
In model-free learning, the model of the environment is _____ and requires exploration.
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Match the following terms with their correct descriptions:
Match the following terms with their correct descriptions:
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What method is often used to sample from the unknown model in reinforcement learning?
What method is often used to sample from the unknown model in reinforcement learning?
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As ε in ε-greedy search decreases, the strategy becomes more exploratory.
As ε in ε-greedy search decreases, the strategy becomes more exploratory.
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Why is it often unrealistic to have perfect knowledge of the environment in reinforcement learning?
Why is it often unrealistic to have perfect knowledge of the environment in reinforcement learning?
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What is the purpose of the softmax function in the context of action selection?
What is the purpose of the softmax function in the context of action selection?
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When the temperature variable T is small, all actions are equally likely to be chosen.
When the temperature variable T is small, all actions are equally likely to be chosen.
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What exploration strategy is mentioned that gradually moves from exploration to exploitation?
What exploration strategy is mentioned that gradually moves from exploration to exploitation?
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In deterministic cases, the equation for Q-value simplifies to $Q(s, a) = ______$.
In deterministic cases, the equation for Q-value simplifies to $Q(s, a) = ______$.
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In the annealing strategy, what happens when T is large?
In the annealing strategy, what happens when T is large?
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The Bellman equation remains unchanged in model-free learning for deterministic rewards.
The Bellman equation remains unchanged in model-free learning for deterministic rewards.
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According to the content, what is used as a backup rule for Q-value updates?
According to the content, what is used as a backup rule for Q-value updates?
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Match the following components with their corresponding descriptions:
Match the following components with their corresponding descriptions:
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What does the variable $eta$ represent in the Q-learning algorithm?
What does the variable $eta$ represent in the Q-learning algorithm?
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Q-learning is an on-policy method that uses policy to determine the next action.
Q-learning is an on-policy method that uses policy to determine the next action.
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What is the purpose of the discount factor $eta$ in Q-learning?
What is the purpose of the discount factor $eta$ in Q-learning?
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In Q-learning, the value of the best next action is used without using the ______.
In Q-learning, the value of the best next action is used without using the ______.
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Match the following algorithms with their characteristics:
Match the following algorithms with their characteristics:
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Which statement about the Sarsa algorithm is true?
Which statement about the Sarsa algorithm is true?
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The Q-learning update rule converges to optimal Q values over time.
The Q-learning update rule converges to optimal Q values over time.
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What happens to the learning rate $eta$ over time in the Q-learning algorithm?
What happens to the learning rate $eta$ over time in the Q-learning algorithm?
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What happens to Q values over time?
What happens to Q values over time?
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In a deterministic environment, the rewards and next states are known.
In a deterministic environment, the rewards and next states are known.
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What is the discount factor (γ) mentioned in the content?
What is the discount factor (γ) mentioned in the content?
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The process of adjusting the value of current actions based on future estimates is called ___________.
The process of adjusting the value of current actions based on future estimates is called ___________.
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Match the following paths with their Q values based on the environment described:
Match the following paths with their Q values based on the environment described:
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In a nondeterministic environment, how do we deal with varying rewards?
In a nondeterministic environment, how do we deal with varying rewards?
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If path A is seen first, the Q value computed will always be higher than if path B is seen first.
If path A is seen first, the Q value computed will always be higher than if path B is seen first.
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What do we do when next states and rewards are nondeterministic?
What do we do when next states and rewards are nondeterministic?
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Study Notes
Reinforcement Learning
- Reinforcement learning is a machine learning approach focused on teaching a computer agent to take optimal actions in an environment through interaction.
- It differs from supervised learning as the agent doesn't receive explicit instructions on the best action at each step.
- Instead, the agent receives numerical rewards (positive or negative) for its actions.
- Reinforcement learning can be viewed as an approach between supervised and unsupervised learning.
Game-Playing and Robot in a Maze
- A machine learning agent can be trained to play chess.
- Using a supervised learner isn't appropriate because it's costly to have a teacher for every possible game position and because the goodness of a move depends on subsequent moves.
- A robot in a maze learns through actions, not immediate feedback.
Elements of Reinforcement Learning (Markov Decision Processes)
- State: Represents the agent's situation at time
- Action: Represents an action taken at time
- Reward: Represents the value received after taking an action, which often changes the agent's state.
- Next state probability: The probability of transitioning to a next state given a state and action.
- Reward probability: The probability of receiving a particular reward given a state and action.
- Initial state(s) and goal state(s): The starting and end points of the learning process for the agent in the learning process.
- Episode (trial): Sequence of actions from the start state to a terminal state (goal state).
- The problem is modeled using a Markov Decision Process (MDP)
Policy and Cumulative Reward
- The policy (π) defines how the agent behaves in an environment
- The policy is a mapping from the environment's states to actions
Finite vs. Infinite Horizon
- Finite-horizon: Agent aims to maximize the expected reward for a specific number of steps (T).
- Infinite-horizon: Agent aims to maximize the expected sum of discounted future rewards, no limit on steps
Infinite Horizon
- Discounting is important to keep the total payoff or expected total reward from getting to the goal state from an episode in finite.
- The agent's behavior changes depending on whether rewards are considered immediate or in the far future.
Bellman's Equation
- Equation that defines the relationship between the value of a state and the value of its possible actions and the corresponding rewards.
- The value of a state is based on the expected rewards for the best possible action in the state.
Model-Based Learning
- Environment (P(St+1 | St, at), p(rt+1 | St, at)) is known.
- Optimal policy using dynamic programming.
- Dynamic programming solves the problem efficiently for model-based setting.
Value Iteration
- Algorithm used to find the optimal policy by iteratively updating the value function
- Values eventually converge and will indicate a policy that maximizes the expected return
- To start with, V(s) is initialized to arbitrary values. The algorithm then iteratively updates the value estimates by considering how rewards from the future will change estimates from the previous steps.
Policy Iteration
- An algorithm that improves the agent's policy until it converges to the optimal policy
- Values are calculated from the policy and are improved
- The algorithm involves alternating between evaluating the policy and improving the policy by taking actions with the highest rewards.
Temporal Difference Learning
- Model-free learning
- The model of the environment is not known or need to be known.
- Values are evaluated from other values in the next states
- Rewards and values in the future are used to update current estimates.
Exploration Strategies (ε-Greedy Search)
- Agent sometimes explores random actions and sometimes chooses the action with the highest current Q-value.
- Exploration is balanced by exploitation using epsilon greedy.
- The exploration rate starts high and then decreases as the number of interactions increases.
Exploration Strategies (Annealing)
- Control parameters used in exploring actions gradually and smoothly moving to exploitation
- A temperature parameter (T) determines the degree of randomness in selecting actions.
Deterministic Rewards and Actions
- In a deterministic scenario, a single transition for each (state, action) pair.
- Rewards and next state of each pair are known and deterministic.
Nondeterministic Rewards and Actions
- Environment has uncertainty (e.g., random movements, opponents)
- Running average of rewards used.
Q-Learning
- Update Q-values without using the policy.
- Off-Policy learning
- The agent always updates the action with the maximum expected value and update the Q value.
Sarsa
- On-Policy learning.
- The policy is used to determine not only the actions but also the next action the agent takes.
Generalization
- In cases where the number of states and actions is very large, creating a look-up table isn't efficient as it may become huge and cause errors.
- Learning algorithms should be able to generalize and make connections. This often involves regressors that will take input and output values and create a function that is an approximation of the true function. The parameters of the regressor can represent learning.
Partially Observable States
- The agent doesn't know the true state of the environment.
- The agent receives observations from the environment to form a believe about the true state of the environment.
The Tiger Problem
- The agent has to decide whether to open the left door or the right door, knowing that one door has a tiger and the other has a treasure.
- The agent receives reward based on the door it opens. The reward is based on the expected rewards based on probability.
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Description
Test your knowledge on key concepts of reinforcement learning, including finite and infinite-horizon models, Bellman's equation, and the role of policies. This quiz will challenge you with questions about algorithms and the effects of discount factors in learning processes.
