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What is Monte Carlo simulation in reinforcement learning?
Monte Carlo simulation is a method used in reinforcement learning to estimate the value of state-action pairs by averaging the returns observed from multiple simulated trajectories.
Monte Carlo simulation is a method used in reinforcement learning to estimate the value of state-action pairs by averaging the returns observed from multiple simulated trajectories.
See lessWhat is the exploration-exploitation dilemma in reinforcement learning?
The exploration-exploitation dilemma refers to the trade-off between exploring unknown actions to discover better strategies and exploiting known actions to maximize immediate rewards.
The exploration-exploitation dilemma refers to the trade-off between exploring unknown actions to discover better strategies and exploiting known actions to maximize immediate rewards.
See lessWhat is deep Q-learning?
Deep Q-learning is a variant of Q-learning that uses deep neural networks to approximate the Q-value function, allowing for more complex state-action representations.
Deep Q-learning is a variant of Q-learning that uses deep neural networks to approximate the Q-value function, allowing for more complex state-action representations.
See lessWhat is Q-learning in reinforcement learning?
Q-learning is a model-free reinforcement learning algorithm that learns to estimate the value of state-action pairs and updates its estimates based on temporal-difference learning.
Q-learning is a model-free reinforcement learning algorithm that learns to estimate the value of state-action pairs and updates its estimates based on temporal-difference learning.
See lessWhat is imitation learning in reinforcement learning?
Imitation learning is a reinforcement learning technique where an agent learns by observing and imitating the actions of an expert or teacher.
Imitation learning is a reinforcement learning technique where an agent learns by observing and imitating the actions of an expert or teacher.
See lessWhat is policy gradient in reinforcement learning?
Policy gradient is a reinforcement learning technique that directly optimizes the policy (strategy) of an agent by updating its parameters in the direction of higher expected rewards.
Policy gradient is a reinforcement learning technique that directly optimizes the policy (strategy) of an agent by updating its parameters in the direction of higher expected rewards.
See lessWhat is deep reinforcement learning?
Deep reinforcement learning combines reinforcement learning with deep learning techniques, using neural networks to approximate complex value functions and policies.
Deep reinforcement learning combines reinforcement learning with deep learning techniques, using neural networks to approximate complex value functions and policies.
See lessWhat is gradient boosting in machine learning?
Gradient boosting is an ensemble learning technique that builds a series of weak learners sequentially, with each learner correcting the errors of the previous ones by fitting to the residuals.
Gradient boosting is an ensemble learning technique that builds a series of weak learners sequentially, with each learner correcting the errors of the previous ones by fitting to the residuals.
See lessWhat is a random forest in machine learning?
A random forest is an ensemble learning technique that consists of a collection of decision trees, where each tree is trained on a random subset of the data and features, and the final prediction is determined by aggregating the predictions of individual trees.
A random forest is an ensemble learning technique that consists of a collection of decision trees, where each tree is trained on a random subset of the data and features, and the final prediction is determined by aggregating the predictions of individual trees.
See lessWhat is a decision tree in machine learning?
A decision tree is a tree-like model that makes decisions based on a series of rules learned from the data, with each internal node representing a decision based on a feature and each leaf node representing a class label or value.
A decision tree is a tree-like model that makes decisions based on a series of rules learned from the data, with each internal node representing a decision based on a feature and each leaf node representing a class label or value.
See less