import os import pytest from copy import deepcopy import torch as th from torchy_baselines import A2C, CEMRL, PPO, SAC, TD3 from torchy_baselines.common.vec_env import DummyVecEnv from torchy_baselines.common.identity_env import IdentityEnvBox MODEL_LIST = [ PPO ] @pytest.mark.parametrize("model_class", MODEL_LIST) def test_save_load(model_class): """ Test if 'save' and 'load' saves and loads model correctly and if 'load_parameters' and 'get_policy_parameters' work correctly :param model_class: (BaseRLModel) A RL model """ env = DummyVecEnv([lambda: IdentityEnvBox(10)]) # create model model = model_class('MlpPolicy', env, policy_kwargs=dict(net_arch=[16]), verbose=1, create_eval_env=True) # test action probability for given (obs, action) pair # Get dictionary of current parameters params = deepcopy(model.get_policy_parameters()) # Modify all parameters to be random values random_params = dict((param_name, th.rand_like(param)) for param_name, param in params.items()) # Update model parameters with the new zeroed values model.load_parameters(random_params) # shared items new_params = model.get_policy_parameters() shared_items = {k: params[k] for k in params if k in new_params and th.all(th.eq(params[k], new_params[k]))} # Check that at least some actions are chosen different now assert not len(shared_items) == len(new_params), "Selected actions did not change " \ "after changing model parameters." params = new_params # Check model.learn(total_timesteps=1000, eval_freq=500) model.save("test_save.zip") model = model.load("test_save") # check if params are still the same after load new_params = model.get_policy_parameters() shared_items = {k: params[k] for k in params if k in new_params and th.all(th.eq(params[k], new_params[k]))} # Check that at least some actions are chosen different now assert len(shared_items) == len(new_params), "Parameters not the same after save and load." os.remove("test_save.zip")