A transition model in reinforcement learning (RL) is a mathematical representation of how an agent’s actions affect the environment. It defines the probability of moving from one state to another when a specific action is taken. This model is a core component of Markov Decision Processes (MDPs), which are used to formalize RL problems. The transition model captures the dynamics of the environment, allowing the agent to predict future states based on its current state and chosen action. For example, in a grid world navigation task, the transition model might specify that moving “up” from a certain cell has a 90% chance of succeeding and a 10% chance of slipping to an adjacent cell due to environmental noise.
The transition model is typically expressed as a function or matrix ( P(s’ | s, a) ), which gives the probability of transitioning to state ( s’ ) from state ( s ) after taking action ( a ). In deterministic environments, this probability is 1 for a single outcome (e.g., a robot moving exactly one meter forward when instructed). In stochastic environments, the model accounts for uncertainty—like a self-driving car that might not perfectly execute a turn due to sensor noise. Developers often use this model in algorithms like value iteration or policy iteration to compute optimal policies, assuming the model is known. For instance, in a board game like chess, a transition model would enumerate all possible board configurations resulting from a player’s move, though in practice, such models are often simplified due to computational constraints.
In practical RL implementations, the transition model plays a critical role in balancing exploration and exploitation. If the model is known, the agent can plan ahead by simulating trajectories (e.g., using Monte Carlo Tree Search in games like Go). However, in many real-world scenarios, the model is unknown, and the agent must learn it from interactions. Model-based RL algorithms, such as Dyna-Q, combine experience replay with learned transition models to improve sample efficiency. For example, a warehouse robot learning to navigate shelves might initially estimate transition probabilities by trial and error, then refine its model over time. Developers working on RL systems must decide whether to assume a known model (for faster computation) or learn it (for adaptability), depending on the problem’s complexity and the availability of environmental data.