A transition model in reinforcement learning (RL) is a framework that predicts the next state of an environment based on the current state and the action taken by the agent. It can be understood as a function that maps the current state and action to the probabilities of reaching different possible next states. This model is essential for model-based RL approaches, where the agent uses the transition model to simulate experiences and improve its decision-making without interacting with the environment every time.
For example, consider a simple grid world where an agent can move up, down, left, or right. The transition model would specify what happens when the agent takes an action in a given state. If the agent is in a particular square and chooses to move north, the model would provide a probability distribution over the possible squares it could occupy next, taking into account various factors like walls, obstacles, or the probability of slipping to a different square. In this scenario, the transition model might specify that moving north from the current square has a 70% chance of the agent moving to the square above and a 30% chance of hitting a wall.
In practice, the transition model can be learned from data interactions with the environment using techniques such as supervised learning. Once trained, the model can help the agent to plan its actions by evaluating potential sequences of states and actions, ultimately leading to improved policies. In contrast to model-free approaches that rely solely on trial-and-error, incorporating a transition model allows for more strategic decision-making, especially in environments where collecting real experiences is costly or time-consuming.