metadata
library_name: hivex
original_train_name: WildfireResourceManagement_difficulty_4_task_0_run_id_1_train
tags:
- hivex
- hivex-wildfire-resource-management
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WRM-PPO-baseline-task-0-difficulty-4
results:
- task:
type: main-task
name: main_task
task-id: 0
difficulty-id: 4
dataset:
name: hivex-wildfire-resource-management
type: hivex-wildfire-resource-management
metrics:
- type: cumulative_reward
value: 180.52263412475585 +/- 90.05154933560763
name: Cumulative Reward
verified: true
- type: collective_performance
value: 73.34980735778808 +/- 33.30569260296172
name: Collective Performance
verified: true
- type: individual_performance
value: 36.9462173461914 +/- 16.558159505779127
name: Individual Performance
verified: true
- type: reward_for_moving_resources_to_neighbours
value: 88.78441047668457 +/- 41.79921581399254
name: Reward for Moving Resources to Neighbours
verified: true
- type: reward_for_moving_resources_to_self
value: 2.90758473277092 +/- 2.9947906681759133
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 0 with difficulty 4 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Wildfire Resource Management
Task: 0
Difficulty: 4
Algorithm: PPO
Episode Length: 500
Training max_steps: 450000
Testing max_steps: 45000
Train & Test Scripts
Download the Environment