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+ ---
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+ language: en
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+ library_name: torch
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+ license: mit
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+ tags:
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+ - table-2
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+ ---
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+
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+ # Model Card for ahalev/mcuu-table-2-kk4vaif1
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+
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+ This model corresponds to run(s) in Table 2, specifically that with the hyperparameters:
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+
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+ **1)** {'scenario': 5, 'forecast_horizon': 6, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01}
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+ **2)** {'scenario': 5, 'forecast_horizon': 12, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01}
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+ **3)** {'scenario': 5, 'forecast_horizon': 24, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01}
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+
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+ ## Usage
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+ ```python
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+ from trainer import Trainer
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+ trainer = Trainer.from_pretrained('ahalev/mcuu-table-2-kk4vaif1')
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+ algo, env = trainer.algo, trainer.env
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+
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+ # Get an action from a random observation
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+ action, _ = algo.policy.get_action(env.observation_space.sample())
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+
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+ # Evaluate the policy over 2920 timesteps
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+ evaluation = trainer.evaluate()
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+ ```
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+
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+ For more information, see the [repo](https://github.com/ahalev/Microgrid-Control-Under-Uncertainty)
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+ and the [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4866653).
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+
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+ This model was created by [@ahalev](https://hf.co/ahalev).