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README.md

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+---
+language: en
+library_name: torch
+license: mit
+tags:
+- table-2
+---
+
+# Model Card for ahalev/mcuu-table-2-kk4vaif1
+    
+This model corresponds to run(s) in Table 2, specifically that with the hyperparameters:
+    
+**1)** {'scenario': 5, 'forecast_horizon': 6, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01}
+**2)** {'scenario': 5, 'forecast_horizon': 12, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01}
+**3)** {'scenario': 5, '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-kk4vaif1')
+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).