Une observable Process de Décision de Markov (OMDP) is a framework used in decision-making processes where outcomes are uncertain. OMDPs extend traditional Markov Decision Processes (MDPs) by allowing for observable states, making them particularly useful in environments where an agent must make decisions based on incomplete information.
In an OMDP, the decision-making scenario is modeled with states, actions, and transitions. However, unlike standard MDPs where the states may not be directly observable, OMDPs assume that the agent can observe certain aspects or features of the environment. This observability enables the agent to make more informed decisions, as it can infer the underlying state basé sur les observations qu'il reçoit.
La définition formelle d'un OMDP inclut :
- États : Les différentes conditions ou configurations de l'environnement.
- Actions : L'ensemble des mouvements ou décisions possibles que l'agent peut prendre.
- Observations : Les informations visibles que l'agent peut percevoir de l'environnement.
- Probabilités de Transition : The likelihood of moving from one state to another given a specific action.
- Fonction de récompense: A function that assigns a numerical value to each state-action pair, guiding the agent towards optimal behavior.
By incorporating observable states, OMDPs facilitate the application of various algorithms for apprentissage par renforcement and planning, allowing for improved performance in complex environments such as robotics, automated systems, and strategic games.