Solutions

Solution objects represent the solution computed by a planning algorithm, and they can be used by the agents to decide how to behave in an uncertain environment with limited resource availability. For example, a solution can be a policy describing the action to execute depending on the environment state. Other examples are collections of policies and finite-state controllers. The toolbox provide generic data structures which represent such solutions, and below we discuss them in more detail for both Markov Decision Processes and Partially Observable Markov Decision Processes.

Solutions for Markov Decision Processes

• deterministic policy
• stochastic policy
• set of policies

Solutions for Partially Observable Markov Decision Processes

• vector-based policy
• deterministic policy graph
• stochastic finite-state controller
• set of policies