How model-based learning is different from reinforcement learning?

To Model or Not to Model Fortunately, in reinforcement learning, a model has a very specific meaning: it refers to the different dynamic states of an environment and how these states lead to a reward. Model-based RL entails constructing such a model.

What is model-based learning?

Definition. Model-based learning is the formation and subsequent development of mental models by a learner. Most often used in the context of dynamic phenomena, mental models organize information about how the components of systems interact to produce the dynamic phenomena.

What is the difference between model-free and model-based reinforcement learning?

“Model-based methods rely on planning as their primary component, while model-free methods primarily rely on learning.” In the context of reinforcement learning (RL), the model allows inferences to be made about the environment.

Which are potential benefits of model based reinforcement learning?

Model-based RL has a strong advantage of being sample efficient. Many models behave linearly at least in the local proximity. This requires very few samples to learn them. Once the model and the cost function are known, we can plan the optimal controls without further sampling.

Is Q-learning model based or model-free?

So, Q-learning is a model-free algorithm. We can immediately observe it uses p(s′,r|s,a), a probability defined by the MDP model. So, policy iteration (a dynamic programming algorithm), which uses the policy improvement algorithm, is a model-based algorithm.

What is policy based reinforcement learning?

Today, we’ll learn a policy-based reinforcement learning technique called Policy Gradients. It means that we directly try to optimize our policy function π without worrying about a value function. We’ll directly parameterize π (select an action without a value function).

Is Model-Based better than model-free?

Algorithms which use a model are called model-based methods, and those that don’t are called model-free. While model-free methods forego the potential gains in sample efficiency from using a model, they tend to be easier to implement and tune.

Is actor critic model-based or model-free?

Algorithms that purely sample from experience such as Monte Carlo Control, SARSA, Q-learning, Actor-Critic are “model free” RL algorithms.

Is TD learning model based?

Model-Based TD-Learning uses TD-Learning to update the current approximation of the value function. A model of the dynamics of the environment is assumed to be known. Also maze problems and other settings where the dynamics of the environment are known fall into the scope of this approach.