An Observable Markov Decision Process (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 based on the observations it receives.
The formal definition of an OMDP includes:
- States: The various conditions or configurations of the environment.
- Actions: The set of possible moves or decisions the agent can make.
- Observations: The visible information the agent can perceive from the environment.
- Transition Probabilities: The likelihood of moving from one state to another given a specific action.
- Reward Function: 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 reinforcement learning and planning, allowing for improved performance in complex environments such as robotics, automated systems, and strategic games.