trainer.train()
if __name__ == '__main__':
sweep_params = {
'seed': [1, 2, 3],
'env': [
HalfCheetahRandDirecEnv, AntRandDirecEnv, HopperRandParamsEnv,
Walker2DRandDirecEnv, HumanoidRandDirec2DEnv, Walker2DRandParamsEnv
],
'rollouts_per_meta_task': [20],
'max_path_length': [100],
'parallel': [True],
'discount': [0.99],
'normalize_adv': [True],
'positive_adv': [False],
'hidden_sizes': [(64, 64)],
'learn_std': [True],
'hidden_nonlinearity': [tf.tanh],
'output_nonlinearity': [None],
'inner_lr': [0.1],
'learning_rate': [1e-3],
'n_itr': [1001],
'meta_batch_size': [40],
'num_inner_grad_steps': [1],
'scope': [None],
}
run_sweep(run_experiment, sweep_params, EXP_NAME, INSTANCE_TYPE)
logger.logkv("iter", i)
logger.logkv("iter_time", timer.time() - start)
logger.dumpkvs()
if i == 0:
sess.graph.finalize()
if __name__ == '__main__':
sweep_params = {
'alg': ['her'],
'seed': [399856203240],
'env': [
'HandManipulateEgg-v0', 'HandManipulatePen-v0',
'HandManipulateBlock-v0'
],
'env': ['HandManipulateEgg-v0'],
# Env Sampling
'fixed_num_of_contact': [7],
# Problem Conf
'obs_type': ['contact', 'full_contact'],
'obs_type': ['full_contact'],
'process_type': ['max_pool', 'pointnet'],
'process_type': ['pointnet'],
'prediction': ['object'],
'feature_dim': [128],
'feature_layer': [0],
}
run_sweep(main, sweep_params, EXP_NAME, INSTANCE_TYPE)
], # Dummy; we don't time out episodes (they time out by themselves)
"gae_lambda": [.99],
"normalize_adv": [True],
"positive_adv": [False],
}
DEFAULT = 'DEFAULT'
parser = argparse.ArgumentParser()
for key, value in sweep_params.items():
parser.add_argument(f'--{key}', default=DEFAULT)
args = parser.parse_args()
for k in vars(args):
v = getattr(args, k)
if not v == DEFAULT:
arg_type = args_type(sweep_params[k][0])
sweep_params[k] = [arg_type(v)]
# DEBUG HPARAMS
if DEBUG:
sweep_params['meta_batch_size'] = [2]
sweep_params['success_threshold'] = [0]
sweep_params['accuracy_threshold'] = [0]
sweep_params['hidden_sizes'] = [(2, )]
sweep_params['backprop_steps'] = [1]
sweep_params['max_path_length'] = [3]
sweep_params['parallel'] = [False]
sweep_params["memory_dim"] = [3] # 2048
sweep_params["instr_dim"] = [4] # 256
run_sweep(run_experiment, sweep_params, sweep_params['prefix'][0], parser,
INSTANCE_TYPE)
'use_cem': [False],
'num_cem_iters': [5],
# Training
'dynamics_learning_rate': [5e-4, 0.001],
'valid_split_ratio': [0.1],
'initial_random_samples': [True],
'initial_sinusoid_samples': [False],
# Dynamics Model
'recurrent': [False],
'num_models': [5],
'dynamics_hidden_nonlinearity': ['swish'],
'dynamics_output_nonlinearity': [None],
'dynamics_hidden_sizes': [(512, 512, 512)],
'dynamic_model_epochs': [50], # UNUSED
'dynamics_buffer_size': [25000],
'backprop_steps': [100],
'weight_normalization_model': [False], # FIXME: Doesn't work
'dynamics_batch_size': [64],
'cell_type': ['lstm'],
# Other
'n_parallel': [1],
'exp_tag': ['parallel-mbmpc']
}
assert config['n_candidates'][0] % config['num_models'][0] == 0 # FIXME: remove constraint
run_sweep(run_experiment, config, EXP_NAME, INSTANCE_TYPE)
'normalize': [False],
'n_itr': [30],
'discount': [0.99],
# Policy
'n_candidates': [128],
'horizon': [5],
'use_cem': [False],
'num_cem_iters': [4],
# Training
'num_rollouts': [20],
'learning_rate': [0.001],
'valid_split_ratio': [0.1],
'rolling_average_persitency': [0.99],
'initial_random_samples': [True],
# Dynamics Model
'recurrent': [False],
'num_models': [3],
'hidden_nonlinearity_model': ['relu'],
'hidden_sizes_model': [(512, 512)],
'dynamic_model_epochs': [200],
'backprop_steps': [100],
'weight_normalization_model': [False], # FIXME: Doesn't work
'batch_size_model': [256],
'cell_type': ['lstm'],
}
run_sweep(run_experiment, config, EXP_NAME)
'num_models': [10],
'dynamics_hidden_sizes': [(512, 512)],
'dyanmics_hidden_nonlinearity': ['relu'],
'dyanmics_output_nonlinearity': [None],
'dynamics_max_epochs': [50],
'dynamics_learning_rate': [1e-3],
'dynamics_batch_size': [128],
'dynamics_buffer_size': [5000],
# Policy
'policy_hidden_sizes': [(64, 64)],
'policy_learn_std': [True],
'policy_hidden_nonlinearity': [tf.tanh],
'policy_output_nonlinearity': [None],
# Meta-Algo
'meta_batch_size': [10], # Note: It has to be multiple of num_models
'rollouts_per_meta_task': [50],
'num_inner_grad_steps': [1],
'inner_lr': [0.001],
'inner_type': ['log_likelihood'],
'step_size': [0.01],
'exploration': [False],
'scope': [None],
'exp_tag': [''], # For changes besides hyperparams
}
run_sweep(run_experiment, sweep_params, EXP_NAME)
