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{
"title": "Reinforcement Learning Mastery: 100 MCQs",
"description": "A comprehensive set of 100 multiple-choice questions on Reinforcement Learning, covering rewards, value functions, and core algorithms.",
"questions": [
{
"id": 1,
"questionText": "In reinforcement learning, what is the reward?",
"options": [
"A vector representing all possible actions",
"A deterministic sequence of states",
"A scalar feedback signal indicating how good the last action was",
"The final goal of the environment"
],
"correctAnswerIndex": 2,
"explanation": "Reward is the immediate scalar feedback from the environment that tells the agent how good its action was."
},
{
"id": 2,
"questionText": "The cumulative sum of future rewards is called:",
"options": [
"Transition probability",
"Value function",
"State space",
"Policy"
],
"correctAnswerIndex": 1,
"explanation": "Value function estimates the expected total (cumulative) reward an agent can get from a state or state-action pair."
},
{
"id": 3,
"questionText": "Which term represents immediate reward at time t?",
"options": [
"s_t",
"v_t",
"π_t",
"r_t"
],
"correctAnswerIndex": 3,
"explanation": "r_t denotes the reward received at the current time step t."
},
{
"id": 4,
"questionText": "Which of the following is TRUE about the value function V(s)?",
"options": [
"It measures reward only at the next step",
"It gives expected cumulative reward starting from state s",
"It is a policy-independent constant",
"It directly outputs the best action"
],
"correctAnswerIndex": 1,
"explanation": "V(s) estimates the expected sum of future rewards starting from state s following a policy π."
},
{
"id": 5,
"questionText": "Discount factor γ is used to:",
"options": [
"Ignore past rewards",
"Increase the reward infinitely",
"Weight future rewards less than immediate rewards",
"Randomize state transitions"
],
"correctAnswerIndex": 2,
"explanation": "Discount factor 0 ≤ γ ≤ 1 ensures future rewards are worth less than immediate ones."
},
{
"id": 6,
"questionText": "Q(s, a) represents:",
"options": [
"Probability of next state",
"Policy mapping",
"Value of taking action a in state s",
"Immediate reward only"
],
"correctAnswerIndex": 2,
"explanation": "Q-function measures expected cumulative reward when taking action a in state s and then following policy π."
},
{
"id": 7,
"questionText": "The difference between expected reward and actual reward is called:",
"options": [
"Discount factor",
"Greedy error",
"Temporal Difference (TD) error",
"Policy gradient"
],
"correctAnswerIndex": 2,
"explanation": "TD error δ = r + γV(s') − V(s) measures how much the predicted value differs from observed reward."
},
{
"id": 8,
"questionText": "Immediate reward is:",
"options": [
"A policy parameter",
"Sum of all future rewards",
"The feedback obtained right after an action",
"Probability of action success"
],
"correctAnswerIndex": 2,
"explanation": "Immediate reward is the feedback signal received immediately after taking an action in a state."
},
{
"id": 9,
"questionText": "Which function tells the value of a state under a policy π?",
"options": [
"Q-value function Q(s,a)",
"Reward function R(s)",
"State value function V(s)",
"Transition function T(s,a)"
],
"correctAnswerIndex": 2,
"explanation": "V(s) gives expected cumulative reward starting from state s under policy π."
},
{
"id": 10,
"questionText": "Which function evaluates both state and action pair?",
"options": [
"V-value function V(s)",
"Discount function γ",
"Q-value function Q(s, a)",
"Reward function R(s)"
],
"correctAnswerIndex": 2,
"explanation": "Q(s,a) evaluates expected cumulative reward for taking action a in state s and then following policy π."
},
{
"id": 11,
"questionText": "What is the purpose of a reward function R(s,a)?",
"options": [
"To define environment dynamics",
"To store past transitions",
"To map states to actions deterministically",
"To provide feedback to agent about quality of actions"
],
"correctAnswerIndex": 3,
"explanation": "Reward function defines the immediate payoff received by the agent for taking an action in a state."
},
{
"id": 12,
"questionText": "Which value function is policy-specific?",
"options": [
"R(s,a)",
"V*(s)",
"Q*(s,a)",
"Vπ(s)"
],
"correctAnswerIndex": 3,
"explanation": "Vπ(s) depends on the specific policy π being followed."
},
{
"id": 13,
"questionText": "What is the difference between V(s) and Q(s,a)?",
"options": [
"V(s) considers only state; Q(s,a) considers state-action pair",
"They are identical",
"V(s) is deterministic; Q(s,a) is random",
"V(s) gives immediate reward; Q(s,a) gives discounted reward"
],
"correctAnswerIndex": 0,
"explanation": "V(s) measures value of a state; Q(s,a) measures value of taking a specific action in that state."
},
{
"id": 14,
"questionText": "If γ=0 in RL, the agent:",
"options": [
"Considers only immediate rewards",
"Maximizes long-term reward",
"Ignores rewards completely",
"Considers all future rewards equally"
],
"correctAnswerIndex": 0,
"explanation": "γ=0 makes the agent short-sighted, focusing only on immediate reward."
},
{
"id": 15,
"questionText": "If γ approaches 1, the agent:",
"options": [
"Stops learning",
"Values future rewards almost as much as immediate rewards",
"Ignores future rewards",
"Becomes random"
],
"correctAnswerIndex": 1,
"explanation": "High γ makes the agent far-sighted, considering long-term consequences."
},
{
"id": 16,
"questionText": "Which formula defines TD learning update for value function?",
"options": [
"V(s) ← r only",
"Q(s,a) ← r + γmax Q(s',a')",
"V(s) ← γ V(s')",
"V(s) ← V(s) + α[r + γV(s') − V(s)]"
],
"correctAnswerIndex": 3,
"explanation": "TD update modifies V(s) toward observed reward plus discounted next state value."
},
{
"id": 17,
"questionText": "In RL, reward shaping is used to:",
"options": [
"Provide additional intermediate rewards to guide learning",
"Simplify environment dynamics",
"Randomize action selection",
"Remove future rewards"
],
"correctAnswerIndex": 0,
"explanation": "Reward shaping helps the agent learn faster by providing informative intermediate feedback."
},
{
"id": 18,
"questionText": "Expected cumulative reward starting from state s and following policy π is:",
"options": [
"Q*(s,a)",
"R(s)",
"Vπ(s)",
"γ(s)"
],
"correctAnswerIndex": 2,
"explanation": "Vπ(s) is the expected sum of discounted rewards under policy π starting at state s."
},
{
"id": 19,
"questionText": "Which reward type encourages agent to achieve long-term goal?",
"options": [
"Random reward",
"Immediate reward only",
"Negative reward only",
"Sparse reward"
],
"correctAnswerIndex": 3,
"explanation": "Sparse or delayed rewards push the agent to consider long-term strategy."
},
{
"id": 20,
"questionText": "Which function gives the best achievable expected reward from a state?",
"options": [
"Immediate reward function R(s)",
"Optimal value function V*(s)",
"Qπ(s,a)",
"Policy function π(s)"
],
"correctAnswerIndex": 1,
"explanation": "V*(s) represents the maximum expected cumulative reward achievable from state s."
},
{
"id": 21,
"questionText": "Q*(s,a) represents:",
"options": [
"Discount factor",
"Maximum expected reward for taking action a in state s and following optimal policy",
"Immediate reward only",
"Transition probability"
],
"correctAnswerIndex": 1,
"explanation": "Q*(s,a) estimates the optimal expected return for a specific state-action pair."
},
{
"id": 22,
"questionText": "If the reward function is poorly designed, the agent may:",
"options": [
"Ignore environment",
"Learn undesired behavior",
"Increase exploration automatically",
"Immediately converge to optimal policy"
],
"correctAnswerIndex": 1,
"explanation": "Incorrect reward leads to reward hacking — agent may maximize reward in unintended ways."
},
{
"id": 23,
"questionText": "Discounted future reward is calculated as:",
"options": [
"γ only",
"r_t only",
"r_t + γ r_{t+1} + γ^2 r_{t+2} + …",
"Sum of unweighted rewards"
],
"correctAnswerIndex": 2,
"explanation": "Discounted sum reduces importance of rewards further in the future using γ."
},
{
"id": 24,
"questionText": "What is the purpose of Q-learning?",
"options": [
"To generate random actions",
"To directly update policy probabilities",
"To learn the optimal action-value function",
"To compute rewards only"
],
"correctAnswerIndex": 2,
"explanation": "Q-learning seeks to learn Q*(s,a) — the optimal expected cumulative reward function."
},
{
"id": 25,
"questionText": "Monte Carlo methods estimate value function using:",
"options": [
"TD error",
"Actual returns from complete episodes",
"Policy gradient",
"Random rewards"
],
"correctAnswerIndex": 1,
"explanation": "Monte Carlo calculates V(s) or Q(s,a) using the sum of rewards observed in full episodes."
},
{
"id": 26,
"questionText": "Bootstrapping in value function estimation refers to:",
"options": [
"Resetting environment every step",
"Estimating current value using future estimated values",
"Using only random actions",
"Ignoring future rewards"
],
"correctAnswerIndex": 1,
"explanation": "Bootstrapping updates estimates using other current estimates (e.g., TD learning)."
},
{
"id": 27,
"questionText": "Which method combines bootstrapping and Monte Carlo ideas for value estimation?",
"options": [
"SARSA",
"Q-learning",
"TD(λ) learning",
"Policy gradient"
],
"correctAnswerIndex": 2,
"explanation": "TD(λ) uses λ parameter to mix Monte Carlo and TD bootstrapping for more stable learning."
},
{
"id": 28,
"questionText": "What does SARSA stand for?",
"options": [
"Stochastic-Action-Reward-State-Algorithm",
"State-Action-Reward-State-Action",
"Supervised-Action-Reward-State-Agent",
"State-Action-Reward-Sequence-Approximation"
],
"correctAnswerIndex": 1,
"explanation": "SARSA updates Q-values using the current state, action, reward, next state, and next action."
},
{
"id": 29,
"questionText": "Which of the following is TRUE about Q-learning?",
"options": [
"It only works for deterministic environments",
"It is on-policy and depends on agent’s current behavior",
"It is off-policy and learns the optimal Q regardless of agent’s actions",
"It ignores rewards completely"
],
"correctAnswerIndex": 2,
"explanation": "Q-learning is off-policy: it learns Q*(s,a) while following a different policy for action selection."
},
{
"id": 30,
"questionText": "Which parameter balances importance of immediate vs future rewards?",
"options": [
"Reward function R",
"Exploration rate ε",
"Learning rate α",
"Discount factor γ"
],
"correctAnswerIndex": 3,
"explanation": "γ determines how much future rewards contribute to current value estimates."
},
{
"id": 31,
"questionText": "A sparse reward environment means:",
"options": [
"Rewards are continuous and immediate",
"Rewards are given infrequently, usually only on goal completion",
"All states give the same reward",
"Rewards are always negative"
],
"correctAnswerIndex": 1,
"explanation": "Sparse reward settings give feedback rarely, making learning more challenging."
},
{
"id": 32,
"questionText": "In value-based RL, what is the primary goal of the agent?",
"options": [
"Minimize immediate reward",
"Maximize cumulative discounted reward",
"Randomly explore environment",
"Reduce state space"
],
"correctAnswerIndex": 1,
"explanation": "The agent selects actions that maximize expected cumulative rewards over time."
},
{
"id": 33,
"questionText": "What is the Bellman equation for V(s)?",
"options": [
"V(s) = γ^t * r_t",
"V(s) = E[r + γV(s’)]",
"V(s) = r only",
"V(s) = max Q(s,a)"
],
"correctAnswerIndex": 1,
"explanation": "Bellman equation expresses value as immediate reward plus discounted expected value of next state."
},
{
"id": 34,
"questionText": "Which function represents long-term expected reward from taking a specific action?",
"options": [
"V(s)",
"γ(s)",
"R(s)",
"Q(s,a)"
],
"correctAnswerIndex": 3,
"explanation": "Q(s,a) evaluates cumulative reward starting with a specific action."
},
{
"id": 35,
"questionText": "Which function estimates the maximum reward achievable from state s?",
"options": [
"Qπ(s,a)",
"Vπ(s)",
"V*(s)",
"R(s)"
],
"correctAnswerIndex": 2,
"explanation": "V*(s) is the optimal value function representing maximum achievable reward."
},
{
"id": 36,
"questionText": "Temporal difference learning updates value estimates using:",
"options": [
"Observed reward + estimated value of next state",
"Random guesses",
"Policy gradient",
"Only immediate reward"
],
"correctAnswerIndex": 0,
"explanation": "TD uses bootstrapping: V(s) ← V(s) + α[r + γV(s') − V(s)]."
},
{
"id": 37,
"questionText": "Which approach requires full episodes to update values?",
"options": [
"TD learning",
"Monte Carlo",
"SARSA",
"Q-learning"
],
"correctAnswerIndex": 1,
"explanation": "Monte Carlo estimates values based on actual returns from complete episodes."
},
{
"id": 38,
"questionText": "Reward shaping is beneficial because it:",
"options": [
"Eliminates exploration",
"Guarantees deterministic policy",
"Removes the discount factor",
"Speeds up learning by giving intermediate rewards"
],
"correctAnswerIndex": 3,
"explanation": "Reward shaping provides guidance to the agent via extra signals."
},
{
"id": 39,
"questionText": "Which of these is a disadvantage of sparse rewards?",
"options": [
"Reward scaling issues",
"Immediate overfitting",
"Slower convergence and learning difficulty",
"Exploration elimination"
],
"correctAnswerIndex": 2,
"explanation": "Sparse rewards provide limited feedback, making learning slower and exploration harder."
},
{
"id": 40,
"questionText": "Which RL method learns directly from Q-values without policy?",
"options": [
"Monte Carlo policy evaluation",
"Value-based methods (e.g., Q-learning)",
"Actor-Critic",
"Policy gradient"
],
"correctAnswerIndex": 1,
"explanation": "Value-based methods estimate Q-values and derive actions via max(Q) instead of learning policy directly."
},
{
"id": 41,
"questionText": "The TD error δ = r + γV(s') − V(s) is used to:",
"options": [
"Update value estimates incrementally",
"Determine next action",
"Select best policy directly",
"Compute discount factor"
],
"correctAnswerIndex": 0,
"explanation": "TD error measures prediction discrepancy to adjust value function gradually."
},
{
"id": 42,
"questionText": "Why is Q*(s,a) considered optimal?",
"options": [
"It gives immediate reward",
"It ignores state transitions",
"It represents maximum expected reward achievable by any policy",
"It is randomly assigned"
],
"correctAnswerIndex": 2,
"explanation": "Q* provides the best action-value estimates regardless of current policy."
},
{
"id": 43,
"questionText": "Which concept allows estimating future rewards without waiting for episode completion?",
"options": [
"Reward clipping",
"Monte Carlo",
"Sparse reward",
"Bootstrapping (TD learning)"
],
"correctAnswerIndex": 3,
"explanation": "Bootstrapping updates values using estimates of next state instead of waiting for full episode."
},
{
"id": 44,
"questionText": "A discount factor γ close to 0 leads to:",
"options": [
"Far-sighted agent",
"Infinite reward accumulation",
"Short-sighted agent focusing on immediate rewards",
"Random action selection"
],
"correctAnswerIndex": 2,
"explanation": "Low γ reduces the weight of future rewards in value estimates."
},
{
"id": 45,
"questionText": "A discount factor γ close to 1 leads to:",
"options": [
"Far-sighted agent valuing future rewards",
"No learning",
"Randomized reward",
"Immediate reward focus"
],
"correctAnswerIndex": 0,
"explanation": "High γ makes the agent long-term focused, considering distant rewards."
},
{
"id": 46,
"questionText": "Which function guides agent behavior by evaluating future reward potential?",
"options": [
"Reward function only",
"State-action mapping",
"Value function",
"Transition function"
],
"correctAnswerIndex": 2,
"explanation": "Value functions estimate future reward potential, indirectly guiding actions."
},
{
"id": 47,
"questionText": "Which method combines state and action evaluation to choose optimal moves?",
"options": [
"TD(0) only",
"Q-function",
"V-function",
"Monte Carlo only"
],
"correctAnswerIndex": 1,
"explanation": "Q(s,a) evaluates expected return for state-action pairs."
},
{
"id": 48,
"questionText": "Which term measures the quality of an action in a state?",
"options": [
"γ",
"Reward shaping",
"Q-value",
"V-value"
],
"correctAnswerIndex": 2,
"explanation": "Q-value estimates long-term expected reward for taking a specific action."
},
{
"id": 49,
"questionText": "Value function approximation is necessary when:",
"options": [
"Actions are discrete",
"State space is small",
"Rewards are deterministic",
"State space is too large or continuous"
],
"correctAnswerIndex": 3,
"explanation": "Large or continuous state spaces make tabular value storage impractical."
},
{
"id": 50,
"questionText": "Which method learns policy indirectly via value estimates?",
"options": [
"Actor-Critic only",
"Value-based RL",
"Monte Carlo only",
"Policy gradient"
],
"correctAnswerIndex": 1,
"explanation": "Value-based methods choose actions via max(Q) without learning policy parameters directly."
},
{
"id": 51,
"questionText": "In a deterministic environment, TD(0) converges to:",
"options": [
"Immediate rewards only",
"Random values",
"True state values V(s)",
"Policy parameters"
],
"correctAnswerIndex": 2,
"explanation": "TD(0) converges to correct V(s) if learning rate and exploration conditions are met."
},
{
"id": 52,
"questionText": "Bootstrapping can introduce bias but reduces:",
"options": [
"Variance in estimates",
"Immediate rewards",
"Policy randomness",
"Learning rate"
],
"correctAnswerIndex": 0,
"explanation": "TD bootstrapping reduces variance at the cost of some bias."
},
{
"id": 53,
"questionText": "The max operator in Q-learning helps:",
"options": [
"Compute TD error only",
"Discount rewards",
"Randomize exploration",
"Choose action with highest estimated return"
],
"correctAnswerIndex": 3,
"explanation": "max_a Q(s’,a) selects the action with highest expected value for next state."
},
{
"id": 54,
"questionText": "Q-learning is considered off-policy because:",
"options": [
"It learns optimal Q regardless of agent’s current actions",
"It uses random rewards only",
"It ignores state transitions",
"It directly follows current policy"
],
"correctAnswerIndex": 0,
"explanation": "Off-policy learning allows learning of Q* while following exploratory policy."
},
{
"id": 55,
"questionText": "Which function provides guidance for immediate action selection?",
"options": [
"V(s)",
"Reward function",
"Discount factor",
"Q(s,a)"
],
"correctAnswerIndex": 3,
"explanation": "Q-values indicate which action in current state yields highest expected reward."
},
{
"id": 56,
"questionText": "Monte Carlo updates are unbiased but have:",
"options": [
"High variance",
"Immediate convergence",
"No error",
"Low variance"
],
"correctAnswerIndex": 0,
"explanation": "Monte Carlo estimates can vary widely between episodes, leading to high variance."
},
{
"id": 57,
"questionText": "Which value function is used in policy iteration to evaluate policy?",
"options": [
"Q*(s,a)",
"R(s)",
"Vπ(s)",
"V*(s)"
],
"correctAnswerIndex": 2,
"explanation": "Policy evaluation uses Vπ(s) to estimate expected return under policy π."
},
{
"id": 58,
"questionText": "Temporal difference methods combine Monte Carlo ideas and:",
"options": [
"Policy gradients",
"Reward clipping",
"Bootstrapping",
"Random exploration"
],
"correctAnswerIndex": 2,
"explanation": "TD methods use bootstrapping to estimate value based on next state’s current value."
},
{
"id": 59,
"questionText": "Sparse rewards make RL more challenging because:",
"options": [
"Policy gradient fails",
"Agent receives little guidance during learning",
"Discount factor becomes irrelevant",
"Agent converges immediately"
],
"correctAnswerIndex": 1,
"explanation": "Without frequent feedback, the agent struggles to learn correct action-value mapping."
},
{
"id": 60,
"questionText": "Which term describes expected future reward from a state-action pair?",
"options": [
"V(s)",
"R(s)",
"Q(s,a)",
"γ"
],
"correctAnswerIndex": 2,
"explanation": "Q(s,a) measures cumulative expected reward starting from that action."
},
{
"id": 61,
"questionText": "Which method updates value functions continuously after every step?",
"options": [
"Monte Carlo",
"Reward shaping",
"TD learning",
"Policy gradient"
],
"correctAnswerIndex": 2,
"explanation": "TD learning updates V(s) incrementally using observed reward and next state value."
},
{
"id": 62,
"questionText": "Which value function guides long-term planning in RL?",
"options": [
"Policy entropy",
"Reward only",
"Immediate next state",
"V(s) and Q(s,a)"
],
"correctAnswerIndex": 3,
"explanation": "V(s) and Q(s,a) provide estimates of cumulative future reward for planning actions."
},
{
"id": 63,
"questionText": "Which is true about bootstrapped TD updates?",
"options": [
"They are only for deterministic environments",
"They ignore discount factor",
"They reduce variance compared to Monte Carlo",
"They eliminate reward function"
],
"correctAnswerIndex": 2,
"explanation": "Bootstrapping reduces variance but introduces bias, unlike full-episode Monte Carlo."
},
{
"id": 64,
"questionText": "Which parameter determines learning step size in TD updates?",
"options": [
"γ (discount factor)",
"ε (exploration)",
"α (learning rate)",
"λ (trace decay)"
],
"correctAnswerIndex": 2,
"explanation": "α controls how much each update adjusts the current value estimate."
},
{
"id": 65,
"questionText": "Which function represents optimal action-value function?",
"options": [
"Vπ(s)",
"Q*(s,a)",
"R(s)",
"V*(s)"
],
"correctAnswerIndex": 1,
"explanation": "Q*(s,a) gives the best achievable return for a state-action pair following optimal policy."
},
{
"id": 66,
"questionText": "Which scenario illustrates reward hacking?",
"options": [
"Agent stops learning",
"Agent explores randomly",
"Agent finds shortcut to maximize reward but violates task intention",
"Agent follows optimal policy"
],
"correctAnswerIndex": 2,
"explanation": "Reward hacking occurs when agent exploits unintended loopholes in reward function."
},
{
"id": 67,
"questionText": "Which function is used to derive greedy action selection?",
"options": [
"Q(s,a)",
"V(s)",
"R(s)",
"γ"
],
"correctAnswerIndex": 0,
"explanation": "Greedy selection picks action with maximum Q-value in current state."
},
{
"id": 68,
"questionText": "Which parameter λ in TD(λ) balances:",
"options": [
"Exploration vs exploitation",
"Monte Carlo vs TD updates",
"Immediate vs sparse reward",
"Learning rate vs discount factor"
],
"correctAnswerIndex": 1,
"explanation": "λ mixes short-term TD updates with long-term Monte Carlo returns."
},
{
"id": 69,
"questionText": "Why are value function approximators needed in large environments?",
"options": [
"State space too large for tabular methods",
"Discount factor irrelevant",
"Policy gradients fail",
"Rewards are deterministic"
],
"correctAnswerIndex": 0,
"explanation": "Function approximation allows generalization when storing values for every state is impossible."
},
{
"id": 70,
"questionText": "Which function measures discrepancy between predicted and observed reward?",
"options": [
"γ",
"Q-value",
"TD error δ",
"V(s)"
],
"correctAnswerIndex": 2,
"explanation": "TD error δ = r + γV(s') − V(s) indicates prediction mismatch for updating values."
},
{
"id": 71,
"questionText": "An agent consistently receives +1 reward only at goal completion. This is an example of:",
"options": [
"Dense reward",
"Negative reward",
"Shaped reward",
"Sparse reward"
],
"correctAnswerIndex": 3,
"explanation": "Sparse reward occurs when feedback is only given at task completion."
},
{
"id": 72,
"questionText": "If Q(s,a) underestimates future rewards, the agent may:",
"options": [
"Avoid valuable actions",
"Ignore discount factor",
"Converge instantly",
"Overexplore"
],
"correctAnswerIndex": 0,
"explanation": "Underestimated Q-values mislead agent to ignore actions with high actual returns."
},
{
"id": 73,
"questionText": "In episodic tasks, value function returns are calculated until:",
"options": [
"First reward",
"Episode ends",
"Discount factor γ=0",
"Next action"
],
"correctAnswerIndex": 1,
"explanation": "Episodic tasks compute total return from start state until terminal state."
},
{
"id": 74,
"questionText": "Expected reward from a state following policy π is given by:",
"options": [
"γ",
"Vπ(s)",
"R(s)",
"Q*(s,a)"
],
"correctAnswerIndex": 1,
"explanation": "Vπ(s) = E[Σ γ^t r_t | s, π] is the formal definition."
},
{
"id": 75,
"questionText": "Q-learning update formula is:",
"options": [
"V(s) ← r only",
"Policy π(s) ← π(s) + α",
"Q(s,a) ← Q(s,a) + α[r + γ max Q(s’,a’) − Q(s,a)]",
"TD error δ = r − V(s)"
],
"correctAnswerIndex": 2,
"explanation": "Q-learning uses max Q of next state to update current action value."
},
{
"id": 76,
"questionText": "Which factor encourages exploration in value-based methods?",
"options": [
"TD error δ",
"ε-greedy policy",
"Discount factor γ",
"Learning rate α"
],
"correctAnswerIndex": 1,
"explanation": "ε-greedy policy selects random actions with small probability to explore new states."
},
{
"id": 77,
"questionText": "Which method estimates Q(s,a) while following the same policy?",
"options": [
"Monte Carlo",
"SARSA (on-policy)",
"TD(λ)",
"Q-learning (off-policy)"
],
"correctAnswerIndex": 1,
"explanation": "SARSA uses next action chosen by current policy for updates."
},
{
"id": 78,
"questionText": "Which technique combines immediate and future reward estimation in TD learning?",
"options": [
"Monte Carlo only",
"Bootstrapping",
"Random policy",
"Greedy selection"
],
"correctAnswerIndex": 1,
"explanation": "Bootstrapping blends observed reward with estimated next state value."
},
{
"id": 79,
"questionText": "Which value function provides the highest possible expected return?",
"options": [
"Immediate reward function R(s)",
"Policy-specific Vπ(s)",
"TD error δ",
"Optimal value function V*(s)"
],
"correctAnswerIndex": 3,
"explanation": "V*(s) represents maximum expected cumulative reward from state s."
},
{
"id": 80,
"questionText": "Reward shaping helps RL agent by:",
"options": [
"Giving intermediate rewards to guide learning",
"Eliminating exploration entirely",
"Forcing deterministic actions",
"Changing discount factor"
],
"correctAnswerIndex": 0,
"explanation": "Shaped rewards provide additional feedback to accelerate learning."
},
{
"id": 81,
"questionText": "An agent in a maze receives +10 only when it reaches the exit, 0 otherwise. Which challenge does it face?",
"options": [
"High variance in rewards",
"Discount factor issues",
"Immediate feedback overload",
"Sparse rewards making learning slow"
],
"correctAnswerIndex": 3,
"explanation": "The agent gets feedback only at the goal, so intermediate steps provide no reward, slowing learning."
},
{
"id": 82,
"questionText": "A delivery robot gets reward for each package delivered but penalty for hitting obstacles. How should reward shaping be applied?",
"options": [
"Add small negative reward for each step to encourage faster delivery",
"Ignore obstacle penalties",
"Increase discount factor to 1",
"Provide reward only at end"
],
"correctAnswerIndex": 0,
"explanation": "Adding small negative step reward incentivizes faster goal completion while maintaining obstacle penalties."
},
{
"id": 83,
"questionText": "In a stock trading simulation, the agent receives reward only when selling stock at profit. What issue arises?",
"options": [
"Overfitting to stock price",
"Discount factor becomes negative",
"Sparse delayed rewards can make learning inefficient",
"Immediate feedback causes instability"
],
"correctAnswerIndex": 2,
"explanation": "Sparse and delayed reward makes it harder for the agent to learn which actions contributed to eventual profit."
},
{
"id": 84,
"questionText": "An agent in a gridworld receives +1 for moving closer to the goal and -1 for moving away. This is an example of:",
"options": [
"Shaped rewards",
"Random rewards",
"Sparse rewards",
"Negative-only rewards"
],
"correctAnswerIndex": 0,
"explanation": "Reward shaping provides continuous guidance, encouraging progress toward the goal."
},
{
"id": 85,
"questionText": "In a self-driving car simulation, if the agent only receives reward at destination, what would help learning?",
"options": [
"Randomizing rewards",
"Removing penalties",
"Adding intermediate rewards for staying in lane and avoiding collisions",
"Reducing discount factor to 0"
],
"correctAnswerIndex": 2,
"explanation": "Intermediate rewards guide agent step-by-step, improving learning efficiency."
},
{
"id": 86,
"questionText": "A robot arm is learning to stack blocks. It receives reward only when the tower is complete. Which method helps?",
"options": [
"Reward shaping with intermediate points for partial stacking",
"Increase exploration to maximum",
"Ignore intermediate failures",
"Reduce learning rate"
],
"correctAnswerIndex": 0,
"explanation": "Providing partial rewards for successful sub-tasks speeds up learning in sparse reward settings."
},
{
"id": 87,
"questionText": "In a scenario where the agent must navigate a dynamic environment with moving obstacles, which approach improves value estimation?",
"options": [
"Monte Carlo only",
"Ignore moving obstacles in rewards",
"Random exploration without value update",
"TD(λ) with bootstrapping for faster updates"
],
"correctAnswerIndex": 3,
"explanation": "TD(λ) allows combining short-term and long-term rewards for more efficient learning in dynamic environments."
},
{
"id": 88,
"questionText": "A drone receives small negative reward for battery usage and positive reward for reaching checkpoints. What does this reward structure achieve?",
"options": [
"Balances energy consumption and goal achievement",
"Only optimizes immediate reward",
"Encourages ignoring battery constraints",
"Maximizes random exploration"
],
"correctAnswerIndex": 0,
"explanation": "The reward function encourages completing goals efficiently while minimizing energy use."
},
{
"id": 89,
"questionText": "In a game, an agent finds a loophole to repeatedly collect small rewards instead of completing main quest. This is called:",
"options": [
"Reward hacking",
"TD error",
"Sparse reward",
"Bootstrapping"
],
"correctAnswerIndex": 0,
"explanation": "Reward hacking occurs when the agent exploits unintended reward sources instead of completing intended tasks."
},
{
"id": 90,
"questionText": "An agent trained with high discount factor γ in a long-horizon task may:",
"options": [
"Fail to explore",
"Focus on long-term rewards, sometimes ignoring immediate gains",
"Focus only on immediate reward",
"Ignore reward function"
],
"correctAnswerIndex": 1,
"explanation": "High γ emphasizes future rewards, making the agent prioritize long-term outcomes."
},
{
"id": 91,
"questionText": "In a simulation where an agent has multiple goals with different rewards, what is crucial for learning correct value estimates?",
"options": [
"Properly scaling rewards to reflect relative importance",
"Randomizing reward signals",
"Ignoring discount factor",
"Using immediate reward only"
],
"correctAnswerIndex": 0,
"explanation": "Scaling rewards ensures that high-priority goals dominate learning without distorting overall behavior."
},
{
"id": 92,
"questionText": "If an agent receives stochastic rewards from the same action, value estimation must account for:",
"options": [
"Ignoring stochasticity",
"Expected value and variance",
"TD error δ=0",
"Immediate reward only"
],
"correctAnswerIndex": 1,
"explanation": "Stochastic rewards require estimating expected return and possibly managing variance to stabilize learning."
},
{
"id": 93,
"questionText": "In multi-step tasks, an agent that overestimates future rewards may:",
"options": [
"Choose risky actions expecting high payoff",
"Always follow short-term reward",
"Ignore environment",
"Fail to update value functions"
],
"correctAnswerIndex": 0,
"explanation": "Overestimation in Q-values can lead to overly optimistic and risky behavior."
},
{
"id": 94,
"questionText": "Which method helps reduce high variance in Monte Carlo returns for episodic tasks?",
"options": [
"Sparse reward only",
"TD bootstrapping",
"Increase learning rate",
"Ignore intermediate rewards"
],
"correctAnswerIndex": 1,
"explanation": "TD bootstrapping uses estimates from next state, reducing variance compared to full-episode returns."
},
{
"id": 95,
"questionText": "A self-learning agent plays a competitive game. It wins small points frequently but big points only on rare strategies. How should rewards be structured?",
"options": [
"Give only big rewards at game end",
"Randomize reward assignment",
"Remove small rewards entirely",
"Balance frequent small rewards and rare big rewards to guide strategy"
],
"correctAnswerIndex": 3,
"explanation": "Balanced reward shaping ensures agent explores both common and rare valuable strategies."
},
{
"id": 96,
"questionText": "Agent operates in continuous state space where exact Q-values cannot be stored. Which approach is needed?",
"options": [
"Monte Carlo with tables",
"Tabular Q-learning",
"Function approximation (e.g., neural networks)",
"Ignore approximation and use TD only"
],
"correctAnswerIndex": 2,
"explanation": "Continuous spaces require approximating value functions to generalize across states."
},
{
"id": 97,
"questionText": "During training, the agent finds a shortcut to maximize reward but violates intended task. To fix this:",
"options": [
"Redesign reward function to reflect intended goals",
"Reduce discount factor to 0",
"Increase exploration only",
"Remove all negative rewards"
],
"correctAnswerIndex": 0,
"explanation": "Proper reward design prevents reward hacking and aligns learning with intended objectives."
},
{
"id": 98,
"questionText": "An agent receives conflicting rewards for two simultaneous objectives. How should value estimates be handled?",
"options": [
"Use weighted combination of rewards for single value estimate",
"Ignore one objective",
"Use random selection",
"Reduce discount factor to 0"
],
"correctAnswerIndex": 0,
"explanation": "Weighted sum ensures both objectives influence learning appropriately."
},
{
"id": 99,
"questionText": "In a delayed reward task, which technique accelerates learning?",
"options": [
"Reward shaping with intermediate milestones",
"Reducing learning rate",
"Ignoring discount factor",
"Random action selection only"
],
"correctAnswerIndex": 0,
"explanation": "Providing intermediate rewards guides agent through long sequences to the final goal."
},
{
"id": 100,
"questionText": "A reinforcement learning agent in an environment with stochastic transitions and rewards can improve value estimation using:",
"options": [
"TD learning with averaging or function approximation",
"Immediate reward only",
"Ignoring stochasticity",
"Random actions without learning"
],
"correctAnswerIndex": 0,
"explanation": "TD methods combined with averaging or function approximation help stabilize learning in stochastic environments."
}
]
}
|