---
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 <code>0</code> with difficulty <code>4</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>

Environment: **Wildfire Resource Management**<br>
Task: <code>0</code><br>
Difficulty: <code>4</code><br>
Algorithm: <code>PPO</code><br>
Episode Length: <code>500</code><br>
Training <code>max_steps</code>: <code>450000</code><br>
Testing <code>max_steps</code>: <code>45000</code><br><br>

Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>
Download the [Environment](https://github.com/hivex-research/hivex-environments)