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---
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library_name: hivex
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original_train_name: DroneBasedReforestation_difficulty_1_task_2_run_id_1_train
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tags:
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- hivex
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- hivex-drone-based-reforestation
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- reinforcement-learning
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- multi-agent-reinforcement-learning
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model-index:
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- name: hivex-DBR-PPO-baseline-task-2-difficulty-1
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results:
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- task:
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type: sub-task
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name: pick_up_seed_at_base
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task-id: 2
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difficulty-id: 1
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dataset:
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name: hivex-drone-based-reforestation
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type: hivex-drone-based-reforestation
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metrics:
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- type: out_of_energy_count
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value: 0.5738862597942352 +/- 0.0598372330317942
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name: Out of Energy Count
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verified: true
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- type: recharge_energy_count
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value: 147.40538289248943 +/- 114.91514898456525
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name: Recharge Energy Count
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verified: true
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- type: cumulative_reward
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value: 15.073969823122024 +/- 9.872171258055769
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name: Cumulative Reward
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verified: true
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---
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This model serves as the baseline for the **Drone-Based Reforestation** environment, trained and tested on task <code>2</code> with difficulty <code>1</code> using the Proximal Policy Optimization (PPO) algorithm.<br><br>Environment: **Drone-Based Reforestation**<br>Task: <code>2</code><br>Difficulty: <code>1</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) |
