Markov Decision Process vs POMDP
Aspect | Fully observable MDP | Partially Observable MDP (POMDP) |
---|---|---|
Agent’s Knowledge of state | Complete knowledge of the current state of the environment. | Incomplete information about the current state of the environment. |
Information of state | The agent knows exactly where it is and what the environment looks like at any given time. | The agent receives observations that are noisy or incomplete indications of the true state. |
Uncertainty | No uncertainty about state transitions or outcomes of actions. | Observations influenced by the underlying state, but may not fully reveal it. |
Example | A game of chess in which both players possess complete information about the positions of all pieces on the board. | Robot navigating in a foggy environment where it can only see objects within a limited range due to reduced visibility. The robot obtains sensor readings that offer limited information about its surroundings, without possessing direct awareness of the complete environment. |
Partially Observable Markov Decision Process (POMDP) in AI
Partially Observable Markov Decision Process (POMDP) is a mathematical framework employed for decision-making in situations of uncertainty, where the decision-maker lacks complete information or noisy information regarding the current state of the environment. POMDPs have broad applicability in diverse domains such as robotics, healthcare, finance, and others.
This article provides an in-depth overview of Partially Observable Markov Decision Processes (POMDPs), their components, mathematical framework, solving strategies, and practical application in maze navigation using Python.
Table of Content
- What is Partially Observable Markov Decision Process (POMDP)?
- Mathematical Framework of Partially Observable Markov Decision Process
- Markov Decision Process vs POMDP
- Strategies for Solving Partially Observable Markov Decision Processes
- Exploring Maze Navigation with Partially Observable Markov Decision Processes in Python
- Conclusion
Pre-Requisites
- Probability theory: Probability theory is applied to POMDPs to model the uncertainty surrounding the observations made by the agent and the changes in state within the environment.
- Markov processes: A Markov process, sometimes referred to as a Markov chain, is a stochastic model that depicts how a system changes over time. It assumes that the system’s future state is solely dependent on its current state and not on the preceding set of events.
- Decision theory: Taking into account the trade-offs between various actions and their possible outcomes, decision theory offers a framework for making decisions under uncertainty.
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