metadata
library_name: hivex
original_train_name: WildfireResourceManagement_difficulty_2_task_0_run_id_2_train
tags:
- hivex
- hivex-wildfire-resource-management
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WRM-PPO-baseline-task-0-difficulty-2
results:
- task:
type: main-task
name: main_task
task-id: 0
difficulty-id: 2
dataset:
name: hivex-wildfire-resource-management
type: hivex-wildfire-resource-management
metrics:
- type: cumulative_reward
value: 115.81460418701172 +/- 26.078782597878128
name: Cumulative Reward
verified: true
- type: collective_performance
value: 51.81407127380371 +/- 18.776135090696116
name: Collective Performance
verified: true
- type: individual_performance
value: 27.154735565185547 +/- 10.446210720821945
name: Individual Performance
verified: true
- type: reward_for_moving_resources_to_neighbours
value: 60.09906158447266 +/- 33.42797696299573
name: Reward for Moving Resources to Neighbours
verified: true
- type: reward_for_moving_resources_to_self
value: 2.2813201546669006 +/- 1.1529928776928962
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 2 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Wildfire Resource Management
Task: 0
Difficulty: 2
Algorithm: PPO
Episode Length: 500
Training max_steps: 450000
Testing max_steps: 45000
Train & Test Scripts
Download the Environment