metadata
library_name: hivex
original_train_name: WildfireResourceManagement_difficulty_7_task_0_run_id_0_train
tags:
- hivex
- hivex-wildfire-resource-management
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WRM-PPO-baseline-task-0-difficulty-7
results:
- task:
type: main-task
name: main_task
task-id: 0
difficulty-id: 7
dataset:
name: hivex-wildfire-resource-management
type: hivex-wildfire-resource-management
metrics:
- type: cumulative_reward
value: 115.5483470916748 +/- 33.54802987989145
name: Cumulative Reward
verified: true
- type: collective_performance
value: 44.748003768920896 +/- 10.01290846231668
name: Collective Performance
verified: true
- type: individual_performance
value: 24.67834577560425 +/- 6.925884463468533
name: Individual Performance
verified: true
- type: reward_for_moving_resources_to_neighbours
value: 64.52305927276612 +/- 22.579827069648683
name: Reward for Moving Resources to Neighbours
verified: true
- type: reward_for_moving_resources_to_self
value: 1.243993028998375 +/- 0.9987439451375053
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 7 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Wildfire Resource Management
Task: 0
Difficulty: 7
Algorithm: PPO
Episode Length: 500
Training max_steps: 450000
Testing max_steps: 45000
Train & Test Scripts
Download the Environment