metadata
library_name: hivex
original_train_name: WindFarmControl_pattern_8_task_1_run_id_2_train
tags:
- hivex
- hivex-wind-farm-control
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-WFC-PPO-baseline-task-1-pattern-8
results:
- task:
type: sub-task
name: avoid_damage
task-id: 1
pattern-id: 8
dataset:
name: hivex-wind-farm-control
type: hivex-wind-farm-control
metrics:
- type: cumulative_reward
value: 4809.995881347656 +/- 59.342603372525524
name: Cumulative Reward
verified: true
- type: avoid_damage_reward
value: 4810.060444335937 +/- 62.5605922005838
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 8 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Wind Farm Control
Task: 1
Pattern: 8
Algorithm: PPO
Episode Length: 5000
Training max_steps: 8000000
Testing max_steps: 8000000
Train & Test Scripts
Download the Environment