metadata
library_name: hivex
original_train_name: OceanPlasticCollection_task_3_run_id_2_train
tags:
- hivex
- hivex-ocean-plastic-collection
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-OPC-PPO-baseline-task-3
results:
- task:
type: sub-task
name: avoid_plastic
task-id: 3
dataset:
name: hivex-ocean-plastic-collection
type: hivex-ocean-plastic-collection
metrics:
- type: cumulative_reward
value: '-2.0388235569000246 +/- 1.647487721687271'
name: Cumulative Reward
verified: true
- type: global_reward
value: 0.0 +/- 0.0
name: Global Reward
verified: true
- type: local_reward
value: '-2.854285717010498 +/- 1.1865717722656555'
name: Local Reward
verified: true
This model serves as the baseline for the Ocean Plastic Collection environment, trained and tested on task 3 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Ocean Plastic Collection
Task: 3
Algorithm: PPO
Episode Length: 5000
Training max_steps: 3000000
Testing max_steps: 150000
Train & Test Scripts
Download the Environment