Jun 14, 2024 · Model-Based RL: Actively builds and refines a model of the environment to predict outcomes and plan actions. Model-Free RL: Does not use an internal model and relies on direct experience and trial-and-error.

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.

Mar 24, 2023 · RL algorithms can be either Model-free (MF) or Model-based (MB). If the agent can learn by making predictions about the consequences of its actions, then it is MB. If it can only learn through experience then it is MF.

Today: what do we do if the dynamics are unknown? What kind of models can we use? Why learn the model? Does it work? Yes! Does it work? higher! No! Can we do better? What if we make a mistake? Can we do better? This will be on HW4! How to replan? Even random sampling can often work well here! That seems like a lot of work...

Jun 30, 2020 · Model-based RL has two main steps. First, we systematically cover approaches to dynamics model learning, including challenges like dealing with stochasticity, uncertainty, partial observability, and temporal abstraction.

Dec 12, 2019 · However, we have learned enough about designing model-based algorithms that it is possible to draw some general conclusions about best practices and common pitfalls. In this post, we will survey various realizations of model-based reinforcement learning methods.

Oct 1, 2018 · 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...

Feb 24, 2025 · Model-Based and Model-Free learning are two fundamental approaches in reinforcement learning. While model-based methods rely on an explicit environment model for efficient planning, model-free methods learn solely from experience, making them more flexible for complex environments.

Feb 24, 2025 · Model-based reinforcement learning is a subclass of reinforcement learning where the agent constructs an internal model of the environment's dynamics and uses it to simulate future states, predict rewards, and optimize actions efficiently.

Nov 8, 2017 · The distinction between model-free and model-based reinforcement learning algorithms corresponds to the distinction psychologists make between habitual and goal-directed control of learned behavioral patterns.