# Probabilistic and Reinforcement Learning Track

2023 International Planning Competition

Race Car Control

Example name RaceCar
Action space Dict
State space Dict

## Description

A race car is required to reach a target from an initial position. The race car is described as a kinematic 2nd order model. The race track is bounded with lines representing the track bounds, hitting one of the bounds is considered a failure and the episode is terminated. The shape of the track is represented by a series of point conected with lines.

## Constants (non-fluents)

Constant Type Desc
X1(b) float32 boundary is the line segment (X1, Y1) -> (X2, Y2)
Y1(b) float32 boundary is the line segment (X1, Y1) -> (X2, Y2)
X2(b) float32 boundary is the line segment (X1, Y1) -> (X2, Y2)
Y2(b) float32 boundary is the line segment (X1, Y1) -> (X2, Y2)
X0 float32 starting x position of car
Y0 float32 starting y position of car
GX float32 x center of goal region
GY float32 y center of goal region
COST float32 cost of fuel, proportional to force
GOAL_REWARD float32 reward upon reaching the goal region
MAX-F float32 maximum force in each direction
MASS float32 mass of the car
DT float32 rhow much time passes between epochs

All of these can be read from the RDDLEnv interface and from the RDDL files.

## Action Space

The actioins are the force (or acceleartion in a kinematic setting) applied to each movement axis of the race car.

Action Type Desc
fx BOX(1, -MAX-F, MAX-F, float32) x force component applied to the car
fy BOX(1, -MAX-F, MAX-F, float32) y force component applied to the car
• MAX-F is available from the RDDLEnv interface and in the RDDL domain and instance.

## State Space

The state space is the positions and velocities of the race car in both axis.

State Type Desc
x Box(1, -inf, inf, float32) x position of car
y Box(1, -inf, inf, float32) y position of car
vx Box(1, inf, inf, float32) c velocity of car
vy Box(1, -inf, inf, float32) y velocity of car

## Rewards

The reward of this domain is the minus the cost of using the engines (min power cost) plus the goal reward if the agent reaches the goal

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