DeepLearning - IQ - 7. Reinforcement Learning
Describe the concept of a Markov decision process (MDP) in reinforcement learning.
Answer: A Markov Decision Process (MDP) is a mathematical framework used to model decision-making in situations where outcomes are uncertain. It consists of states, actions, a transition function that defines the probabilities of moving from one state to another after taking an action, and a reward function that provides feedback for each action.
What is a policy in reinforcement learning?
Answer: A policy in reinforcement learning is a strategy or a mapping from states to actions. It defines the agent's behavior, specifying the action the agent should take in each state. The goal is to learn an optimal policy that maximizes the cumulative reward over time.
What is deep reinforcement learning, and how does it differ from traditional reinforcement learning?
Answer: Deep Reinforcement Learning involves using deep neural networks to approximate value functions or policies in reinforcement learning. Traditional RL methods may struggle with high-dimensional state spaces, while deep RL leverages the representation power of neural networks to handle complex environments.
What is the difference between exploration and exploitation in reinforcement learning?
Answer: Exploration involves trying out new actions to discover their effects and gather information about the environment. Exploitation involves choosing actions that are known to yield high rewards based on current knowledge. Balancing exploration and exploitation is crucial for effective learning in RL.
Explain the concepts of an agent, environment, actions, and rewards in reinforcement learning.
Answer: In reinforcement learning, the agent is the learner or decision-maker, the environment is the external system the agent interacts with, actions are the decisions or moves the agent can make, and rewards are the feedback signals that the agent receives from the environment, indicating the desirability of its actions.
What is reinforcement learning (RL)?
Answer: Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or punishments based on its actions, and the goal is to learn a strategy (policy) that maximizes cumulative rewards over time.
Describe the concept of temporal difference (TD) learning in reinforcement learning.
Answer: Temporal Difference (TD) learning is a reinforcement learning approach that updates value estimates based on the difference between the current estimate and the estimate for the next state. It combines ideas from Monte Carlo methods and dynamic programming and is used for model-free learning.
What is the exploration-exploitation dilemma in reinforcement learning, and how can it be addressed?
Answer: The exploration-exploitation dilemma refers to the challenge of deciding whether to explore new actions to gather information or exploit current knowledge for maximizing immediate rewards. Techniques such as epsilon-greedy strategies, UCB (Upper Confidence Bound), and Thompson Sampling are used to address this dilemma.
Explain the concept of a reward function in reinforcement learning.
Answer: The reward function in reinforcement learning assigns a numerical value to each state-action pair or state transition. It provides feedback to the agent about the immediate desirability of its actions. The goal of the agent is to learn a policy that maximizes the cumulative rewards over time.
Explain the terms "value function" and "Q-function" in reinforcement learning.
Answer: The value function estimates the expected cumulative reward an agent can receive from a given state under a specific policy. The Q-function (or action-value function) estimates the expected cumulative reward of taking a specific action in a given state and following a particular policy thereafter.
