mcuu-table-2-j09wmn4i

Model Card for ahalev/mcuu-table-2-j09wmn4i

This model corresponds to run(s) in Table 2, specifically that with the hyperparameters:

1) {'scenario': 2, 'forecast_horizon': 6, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01} 2) {'scenario': 2, 'forecast_horizon': 12, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01} 3) {'scenario': 2, 'forecast_horizon': 24, 'intrinsic_reward_weight': 0.0001, 'bound_reward_weight': 'cosine', 'noise_std': 0.01}

Usage

from trainer import Trainer
trainer = Trainer.from_pretrained('ahalev/mcuu-table-2-j09wmn4i')
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 and the paper.

This model was created by @ahalev.