metadata
library_name: hivex
original_train_name: AerialWildfireSuppression_difficulty_1_task_7_run_id_0_train
tags:
- hivex
- hivex-aerial-wildfire-suppression
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-AWS-PPO-baseline-task-7-difficulty-1
results:
- task:
type: sub-task
name: find_fire
task-id: 7
difficulty-id: 1
dataset:
name: hivex-aerial-wildfire-suppression
type: hivex-aerial-wildfire-suppression
metrics:
- type: crash_count
value: 0.05108651416376233 +/- 0.051039308459031006
name: Crash Count
verified: true
- type: cumulative_reward
value: 89.32066116333007 +/- 11.08362856060902
name: Cumulative Reward
verified: true
This model serves as the baseline for the Aerial Wildfire Suppression environment, trained and tested on task 7 with difficulty 1 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Aerial Wildfire Suppression
Task: 7
Difficulty: 1
Algorithm: PPO
Episode Length: 3000
Training max_steps: 1800000
Testing max_steps: 180000
Train & Test Scripts
Download the Environment