---
language: en
library_name: torch
license: mit
tags:
- table-2
---

# Model Card for ahalev/mcuu-table-2-vqtfc2dn
    
This model corresponds to run(s) in Table 2, specifically that with the hyperparameters:
    
**1)** {'scenario': 3, 'forecast_horizon': 6, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01}
**2)** {'scenario': 3, 'forecast_horizon': 12, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01}
**3)** {'scenario': 3, 'forecast_horizon': 24, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01}
    
## Usage
```python
from trainer import Trainer
trainer = Trainer.from_pretrained('ahalev/mcuu-table-2-vqtfc2dn')
algo, env = trainer.algo, trainer.env

# Get an action from a random observation
action, _ = algo.policy.get_action(env.observation_space.sample())

# Evaluate the policy over 2920 timesteps
evaluation = trainer.evaluate()
```

For more information, see the [repo](https://github.com/ahalev/Microgrid-Control-Under-Uncertainty)
and the [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4866653).
    
This model was created by [@ahalev](https://hf.co/ahalev).