Um Observável Processo de Decisão 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 com base nas observações que recebe.
A definição formal de um OMDP inclui:
- Estados: As várias condições ou configurações do ambiente.
- Ações: O conjunto de movimentos ou decisões possíveis que o agente pode fazer.
- Observações: As informações visíveis que o agente pode perceber do ambiente.
- Probabilidades de Transição: The likelihood of moving from one state to another given a specific action.
- Função de Recompensa: 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 aprendizado por reforço and planning, allowing for improved performance in complex environments such as robotics, automated systems, and strategic games.