Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning

Code accompanying the paper: https://arxiv.org/abs/2103.15084

To train a quantum agent on the CartPole environment, run run_quantum.py and set the hyperparameters in the file:

hyperparams = {
    'episodes': [5000],
    'batch_size': [16],
    'epsilon': [1],
    'epsilon_decay': [0.99],
    'epsilon_min': [0.01],
    'gamma': [0.99],
    'update_after': [1],
    'update_target_after': [1],
    'learning_rate': [0.001],
    'learning_rate_in': [0.001],
    'learning_rate_out': [0.1],
    'circuit_depth': [5],
    'epsilon_schedule': ['fast'],
    'use_reuploading': True,
    'trainable_scaling': True,
    'trainable_output': True,
    'output_factor': 1,
    'reps': 10,
    'env': Envs.CARTPOLE,
    'save': True,
    'test': False
}