# Defining Static (PO)MDP Properties

The definition of a (PO)MDP includes several static properties, which are defined with the functions listed in this section. This section is an overview, with links to the docstrings for detailed usage information.

To use most solvers, it is only necessary to implement a few of these functions.

## Spaces

The state, action and observation spaces are defined by the following functions:

The object returned by these functions should implement part or all of the interface for spaces. For discrete problems, a vector is appropriate.

It is often important to limit the action space based on the current state, belief, or observation. This can be accomplished with the actions(m, s) or actions(m, b) function. See Histories associated with a belief and the history and currentobs docstrings for more information.

## Initial Distributions

initialstate(pomdp) should return the distribution of the initial state, either as an explicit distribution (e.g. a POMDPModelTools.SparseCat) that conforms to the distribution interface or with a POMDPModelTools.ImplicitDistribution to easily specify a function to sample from the space.

initialobs(pomdp, state) is used to return the distribution of the initial observation in occasional cases where the policy expects an initial observation rather than an initial belief, e.g. in a reinforcement learning setting. It is not used in a standard POMDP simulation.

## Discount Factor

discount(pomdp) should return a number between 0 and 1 to define the discount factor.

## Terminal States

If a problem has terminal states, they can be specified using the isterminal function. If a state s is terminal isterminal(pomdp, s) should return true, otherwise it should return false.

In POMDPs.jl, no actions can be taken from terminal states, and no additional rewards can be collected, thus, the value function for a terminal state is zero. POMDPs.jl does not have a mechanism for defining terminal rewards apart from the reward function, so the problem should be defined so that any terminal rewards are collected as the system transitions into a terminal state.

## Indexing

For discrete problems, some solvers rely on a fast method for finding the index of the states, actions, or observations in an ordered list. These indexing functions can be implemented as

Note

The converse mapping (from indices to states) is not part of the POMDPs interface. A solver will typically create a vector containing all the states to define it.

Note

There is no requirement that the object returned by the space functions above respect the same ordering as the index functions. The index functions are the sole definition of ordering of the states. The POMDPModelTools package contains convenience functions for constructing a list of states that respects the ordering specified by the index functions. For example, POMDPModelTools.ordered_states returns an AbstractVector of the states in the order specified by stateindex.

## Conversion to vector types

Some solvers (notably those that involve deep learning) rely on the ability to represent states, actions, and observations as vectors. To define a mapping between vectors and custom problem-specific representations, implement the following functions (see docstring for signature):