[map]
mapSize = 6
mapMaxSize = 50
randomMap = 1
loadMap = experience1
[robot]
initial_pose_x = random
initial_pose_z = random
number_of_actions = 9
sensor_noise = 0
[general]
record_robot_path = Yes
record_state_history = Yes
Given a robot standing in room with task reaching a destination under cartesian coordinates, defined as:
The robot can move Up/Down/Left/Right one step at a time in a 2d grid. The robot has a localization algorithm (AMCL) active at all times.
A default Markov Decision Process is defined such as:
- S = Set of states regarded as the state space.
- A = Set of actions to transition from one state to another
- P_a(S,S') = Transition probability from state S to state S' due a.
- R_a(S,S') = regard received after transitioning from state S to state S' due to a.
Solving the MDP provide us with a policy which maximices reward.
- Probability transaction matrix depend on the context.
- Policies reflect a (hypothesis/context) reality.
- There exists future states that are not defined at time t of the mdp.
- S = { (x,y,theta,Lost), (x,y,theta,localized), arrivedDestination}
- A = { MoveUp, MoveDown, MoveLeft, MoveRight, Stay }
The initial default MDP data for P_a(S,S') and R_a(S,S') can be found here