metadata
library_name: hivex
original_train_name: WindFarmControl_pattern_3_task_1_run_id_1_train
tags:
- hivex
- hivex-wind-farm-control
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WFC-PPO-baseline-task-1-pattern-3
results:
- task:
type: sub-task
name: avoid_damage
task-id: 1
pattern-id: 3
dataset:
name: hivex-wind-farm-control
type: hivex-wind-farm-control
metrics:
- type: cumulative_reward
value: 4824.08486328125 +/- 32.716714052830234
name: Cumulative Reward
verified: true
- type: avoid_damage_reward
value: 4824.054873046875 +/- 34.55972201083332
name: Avoid Damage Reward
verified: true
- type: individual_performance
value: 0.0 +/- 0.0
name: Individual Performance
verified: true
This model serves as the baseline for the Wind Farm Control environment, trained and tested on task 1 with pattern 3 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Wind Farm Control
Task: 1
Pattern: 3
Algorithm: PPO
Episode Length: 5000
Training max_steps: 8000000
Testing max_steps: 8000000
Train & Test Scripts
Download the Environment