metadata
library_name: hivex
original_train_name: OceanPlasticCollection_task_0_run_id_0_train
tags:
- hivex
- hivex-ocean-plastic-collection
- reinforcement-learning
- multi-agent-reinforcement-learning
model-index:
- name: hivex-OPC-PPO-baseline-task-0
results:
- task:
type: main-task
name: main_task
task-id: 0
dataset:
name: hivex-ocean-plastic-collection
type: hivex-ocean-plastic-collection
metrics:
- type: cumulative_reward
value: 823.2983947753906 +/- 197.42713024318527
name: Cumulative Reward
verified: true
- type: global_reward
value: 285.8913818359375 +/- 84.43798423128938
name: Global Reward
verified: true
- type: local_reward
value: 158.69510040283203 +/- 32.16273712262643
name: Local Reward
verified: true
This model serves as the baseline for the Ocean Plastic Collection environment, trained and tested on task 0 using the Proximal Policy Optimization (PPO) algorithm.
Environment: Ocean Plastic Collection
Task: 0
Algorithm: PPO
Episode Length: 5000
Training max_steps: 3000000
Testing max_steps: 150000
Train & Test Scripts
Download the Environment