Python generate_experiment_info示例

示例#1

}

algorithm['dynamics'] = {
    'type': DynamicsLRPrior,
    'regularization': 1e-6,
    'prior': {
        'type': DynamicsPriorGMM,
        'max_clusters': 20,
        'min_samples_per_cluster': 40,
        'max_samples': 20,
    },
}

algorithm['traj_opt'] = {
    'type': TrajOptLQRPython,
}

algorithm['policy_opt'] = {}

config = {
    'iterations': algorithm['iterations'],
    'num_samples': 5,
    'verbose_trials': 0,
    'common': common,
    'agent': agent,
    'gui_on': True,
    'algorithm': algorithm,
}

common['info'] = generate_experiment_info(config)

示例#2

文件： hyperparams.py 项目： abhishekunique/gps

algorithm['traj_opt'] = {
    'type': TrajOptLQRPython,
}

algorithm['policy_opt'] = {
    'type': PolicyOptTf,
    'weights_file_prefix': EXP_DIR + 'policy',
    'iterations': 3000,
}

algorithm['policy_prior'] = {
    'type': PolicyPriorGMM,
    'max_clusters': 20,
    'min_samples_per_cluster': 40,
    'max_samples': 40,
}

config = {
    'iterations': algorithm['iterations'],
    'common': common,
    'verbose_trials': 0,
    'verbose_policy_trials': 1,
    'agent': agent,
    'gui_on': True,
    'algorithm': algorithm,
    'num_samples': 5,
}

common['info'] = generate_experiment_info(config)