def objective(self, x):
model = GMM()
model.copy_model(self.initial_model)
model.update_gaussians(np.asarray(x))
accuracy, mean_return, mean_length = model.evaluate(self.env,
max_steps=600,
num_episodes=1)
print("Accuracy:", accuracy, "mean_return:", mean_return)
return -mean_return
def main(cfg):
logger = logging.getLogger('tacto.renderer')
logger.propagate = False
env = custom_sawyer_peg_env(cfg.env)
for model_name in cfg.model_names:
print(model_name)
model = GMM(add_cwd(model_name))
accuracy, mean_return, mean_length = model.evaluate(env=env, **cfg.test)
logger = logging.getLogger(__name__)
logger.info("Accuracy: %.2f, Mean return: %.2f, mean length: %.2f" % (accuracy, mean_return, mean_length))
def main(cfg):
logger = logging.getLogger('tacto.renderer')
logger.propagate = False
logger = logging.getLogger('env.sawyer_peg_env')
logger.propagate = False
logger = logging.getLogger('pybulletX._wrapper')
logger.propagate = False
#Hyperparams
type = "pose" # "pose" or "force"
demonstration_dir = add_cwd("demonstrations_txt")
K = 3
budget = 20
#Start matlab
log_likelihood = []
best_ret = 0
if not drlfads.USE_MATLAB:
raise NotImplementedError(f'This function requires matlab')
eng = matlab.engine.start_matlab()
eng.addpath(add_cwd(str(Path(__file__).parents[0])))
env = custom_sawyer_peg_env(cfg.env)
for _ in range(budget):
name = "gmm_peg_%s_%d" % (type, K)
bll = eng.train_model(demonstration_dir, name, type, K, 1)
print("model trained, final log likelihood:", bll)
# Test new configurations
if not bll in log_likelihood:
# Evaluate model in actual environment
log_likelihood.append(bll)
model = GMM(name + ".mat")
accuracy, mean_return, mean_length = model.evaluate(env=env,
**cfg.test)
print("Accuracy:", accuracy, "Mean return:", mean_return,
"Mean length:", mean_length)
if mean_return > best_ret:
print("Best model so far!")
best_ret = mean_return
model.save_model(name + ".npy")
eng.quit()