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()
def main():
# Environment hyperparameters
env_params = {
"show_gui": False,
"with_force": False,
"with_joint": False,
"relative": True,
"with_noise": False,
"dt": 0.05
}
env = custom_sawyer_peg_env(**env_params)
# Evaluation parameters
model_name = "models/GMM_models/gmm_peg_v2_pose_9.npy"
model = GMM(model_name)
optimizer = GMMOptimizer(env, model)
res = optimizer.optimize()
print(res.x)
model.update_gaussians(np.asarray(res.x))
new_model_name = "models/optimizer/test.npy"
model.save_model(new_model_name)
print("Best model - Average reward:", -res.fun)
print("Model saved as:", new_model_name)