metadata
library_name: hivex
original_train_name: WildfireResourceManagement_difficulty_4_task_1_run_id_1_train
tags:
- hivex
- hivex-wildfire-resource-management
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WRM-PPO-baseline-task-1-difficulty-4
results:
- task:
type: sub-task
name: keep_all
task-id: 1
difficulty-id: 4
dataset:
name: hivex-wildfire-resource-management
type: hivex-wildfire-resource-management
metrics:
- type: cumulative_reward
value: 417.6073974609375 +/- 190.19011561647912
name: Cumulative Reward
verified: true
- type: collective_performance
value: 72.88476066589355 +/- 36.648931872523086
name: Collective Performance
verified: true
- type: individual_performance
value: 37.18313026428223 +/- 17.18048840702159
name: Individual Performance
verified: true
- type: reward_for_moving_resources_to_neighbours
value: 1.0694182068109512 +/- 0.452972812288188
name: Reward for Moving Resources to Neighbours
verified: true
- type: reward_for_moving_resources_to_self
value: 324.34189453125 +/- 153.73020446695705
name: Reward for Moving Resources to Self
verified: true
This model serves as the baseline for the Wildfire Resource Management environment, trained and tested on task 1 with difficulty 4 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Wildfire Resource Management
Task: 1
Difficulty: 4
Algorithm: PPO
Episode Length: 500
Training max_steps: 450000
Testing max_steps: 45000
Train & Test Scripts
Download the Environment