Probabilistic and Reinforcement Learning Track

2023 International Planning Competition

Fire Fighting

   
Example name Wildfire
Action space Dict
State space Dict

Description

Imagine that you are an emergency manager tasked to control a wildfire, as depicted in the animation on the right, and ultimately keep it away from important target locations, such as schools or residential houses. Consider that each cell on the map can either be burning (flame), out-of-fuel (gray), or neither (green) and you have the ability to both cut-out fuel from a non-target cell (causing it to be out-of-fuel and preventing it from igniting in the future) and put-out the fire occupying a burning cell. Cells are more likely to ignite as the number of their burning neighbouring cells increases

Constant Type Desc
COST-CUTOUT float32 Cost to cut-out fuel from a cell
COST-PUTOUT float32 Cost to put-out a fire from a cell
PENALTY-TARGET-BURN float32 Penalty for each target cell that is burning
PENALTY-NONTARGET-BURN float32 Penalty for each non-target cell that is burning
NEIGHBOR(x-pos, y-pos, x-pos, y-pos) bool Topology of the cells
TARGET(x-pos, y-pos) bool High value cells that should be protected from fire

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

Action Space

Action Type Desc
put-out(x-pos, y-pos) Discrete(2) actions to put-out out the fire
cut-out(x-pos, y-pos) Discrete(2) cut-out out the fuel

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

State Space

State Type Desc
burning(x-pos, y-pos) Discrete(2) cell currently on fire
out-of-fuel(x-pos, y-pos) Discrete(2) cell does not have fuel to burn (i.e., cut-out or already burned)

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

Rewards

The reward at each steps is a sum of four components

References


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