metadata
library_name: hivex
original_train_name: WildfireResourceManagement_difficulty_2_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-2
results:
- task:
type: sub-task
name: keep_all
task-id: 1
difficulty-id: 2
dataset:
name: hivex-wildfire-resource-management
type: hivex-wildfire-resource-management
metrics:
- type: cumulative_reward
value: 274.1446533203125 +/- 64.14741483958754
name: Cumulative Reward
verified: true
- type: collective_performance
value: 47.04243335723877 +/- 13.647734597375532
name: Collective Performance
verified: true
- type: individual_performance
value: 25.11213264465332 +/- 7.222221709728652
name: Individual Performance
verified: true
- type: reward_for_moving_resources_to_neighbours
value: 1.1445139467716217 +/- 0.3653539966236824
name: Reward for Moving Resources to Neighbours
verified: true
- type: reward_for_moving_resources_to_self
value: 215.85740509033204 +/- 65.10330940872429
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 2 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Wildfire Resource Management
Task: 1
Difficulty: 2
Algorithm: PPO
Episode Length: 500
Training max_steps: 450000
Testing max_steps: 45000
Train & Test Scripts
Download the Environment