metadata
library_name: hivex
original_train_name: WildfireResourceManagement_difficulty_1_task_2_run_id_1_train
tags:
- hivex
- hivex-wildfire-resource-management
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WRM-PPO-baseline-task-2-difficulty-1
results:
- task:
type: sub-task
name: distribute_all
task-id: 2
difficulty-id: 1
dataset:
name: hivex-wildfire-resource-management
type: hivex-wildfire-resource-management
metrics:
- type: cumulative_reward
value: 621.6318466186524 +/- 263.80346369019475
name: Cumulative Reward
verified: true
- type: collective_performance
value: 34.165497875213624 +/- 11.603925138407758
name: Collective Performance
verified: true
- type: individual_performance
value: 18.202730894088745 +/- 6.918594494920972
name: Individual Performance
verified: true
- type: reward_for_moving_resources_to_neighbours
value: 513.3224952697753 +/- 252.25941103189368
name: Reward for Moving Resources to Neighbours
verified: true
- type: reward_for_moving_resources_to_self
value: 0.18221199735999108 +/- 0.07358971280652664
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 2 with difficulty 1 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Wildfire Resource Management
Task: 2
Difficulty: 1
Algorithm: PPO
Episode Length: 500
Training max_steps: 450000
Testing max_steps: 45000
Train & Test Scripts
Download the Environment