File size: 1,633 Bytes
e0b08a4 |
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 |
---
library_name: hivex
original_train_name: DroneBasedReforestation_difficulty_5_task_2_run_id_2_train
tags:
- hivex
- hivex-drone-based-reforestation
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-DBR-PPO-baseline-task-2-difficulty-5
results:
- task:
type: sub-task
name: pick_up_seed_at_base
task-id: 2
difficulty-id: 5
dataset:
name: hivex-drone-based-reforestation
type: hivex-drone-based-reforestation
metrics:
- type: out_of_energy_count
value: 0.5702619183063508 +/- 0.07710102619282291
name: Out of Energy Count
verified: true
- type: recharge_energy_count
value: 164.77283651448786 +/- 105.66051729333431
name: Recharge Energy Count
verified: true
- type: cumulative_reward
value: 14.895887972116471 +/- 7.701409987004873
name: Cumulative Reward
verified: true
---
This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>2</code> with difficulty <code>5</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>2</code><br>Difficulty: <code>5</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)
[hivex-paper]: https://arxiv.org/abs/2501.04180 |