File size: 1,798 Bytes
3f7ae1a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
---
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_3_task_6_run_id_1_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-6-difficulty-3
results:
- task:
type: sub-task
name: explore_furthest_distance_and_return_to_base
task-id: 6
difficulty-id: 3
dataset:
name: hivex-drone-based-reforestation
type: hivex-drone-based-reforestation
metrics:
- type: furthest_distance_explored
value: 146.28668838500977 +/- 16.658292228774815
name: Furthest Distance Explored
verified: true
- type: out_of_energy_count
value: 0.595357158780098 +/- 0.08324645242738359
name: Out of Energy Count
verified: true
- type: recharge_energy_count
value: 131.29083390399813 +/- 117.44315350412963
name: Recharge Energy Count
verified: true
- type: cumulative_reward
value: 6.076253048032522 +/- 5.155621265658263
name: Cumulative Reward
verified: true
---
This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>6</code> with difficulty <code>3</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>6</code><br>Difficulty: <code>3</code><br>Algorithm: <code>PPO</code><br>Episode Length: <code>2000</code><br>Training <code>max_steps</code>: <code>1200000</code><br>Testing <code>max_steps</code>: <code>300000</code><br><br>Train & Test [Scripts](https://github.com/hivex-research/hivex)<br>Download the [Environment](https://github.com/hivex-research/hivex-environments) |