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--- |
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library_name: hivex |
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original_train_name: OceanPlasticCollection_task_1_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-1 |
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results: |
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- task: |
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type: sub-task |
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name: find_highest_polluted_area |
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task-id: 1 |
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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: 994.6653747558594 +/- 158.13702190020126 |
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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: 226.50474700927734 +/- 57.553598550844015 |
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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: 142.19907608032227 +/- 19.368785745326573 |
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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>1</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>1</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) |
