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--- |
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library_name: hivex |
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original_train_name: OceanPlasticCollection_task_0_run_id_0_train |
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tags: |
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- hivex |
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- hivex-ocean-plastic-collection |
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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-OPC-PPO-baseline-task-0 |
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results: |
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- task: |
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type: main-task |
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name: main_task |
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task-id: 0 |
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dataset: |
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name: hivex-ocean-plastic-collection |
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type: hivex-ocean-plastic-collection |
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metrics: |
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- type: cumulative_reward |
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value: 823.2983947753906 +/- 197.42713024318527 |
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name: "Cumulative Reward" |
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verified: true |
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- type: global_reward |
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value: 285.8913818359375 +/- 84.43798423128938 |
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name: "Global Reward" |
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verified: true |
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- type: local_reward |
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value: 158.69510040283203 +/- 32.16273712262643 |
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name: "Local Reward" |
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verified: true |
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--- |
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This model serves as the baseline for the **Ocean Plastic Collection** environment, trained and tested on task <code>0</code> using the Proximal Policy Optimization (PPO) algorithm.<br> |
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<br> |
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Environment: **Ocean Plastic Collection**<br> |
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Task: <code>0</code><br> |
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Algorithm: <code>PPO</code><br> |
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Episode Length: <code>5000</code><br> |
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Training <code>max_steps</code>: <code>3000000</code><br> |
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Testing <code>max_steps</code>: <code>150000</code><br> |
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<br> |
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Train & Test [Scripts](https://github.com/hivex-research/hivex)<br> |
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Download the [Environment](https://github.com/hivex-research/hivex-environments) |
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[hivex-paper]: https://arxiv.org/abs/2501.04180 |
