def main(cfg):
env = custom_sawyer_peg_env(cfg.env)
agent = SAC_Agent(env, **cfg.agent)
agent.load(add_cwd(cfg.test.model_name))
stats = agent.evaluate(**cfg.test.run)
logger = logging.getLogger(__name__)
logger.info(stats)
def main(cfg):
logger = logging.getLogger('tacto.renderer')
logger.propagate = False
env = custom_sawyer_peg_env(cfg.env)
pd = PegPD(env)
succesful_episodes = 0
while succesful_episodes < cfg.number_demonstrations:
episode_return = 0
observation = env.reset()
observations = [dict_arrays_to_list(observation)]
pd.reset()
for t in range(env.max_episode_steps):
action = pd.get_action()
observation, reward, done, info = env.step(action)
observations.append(dict_arrays_to_list(observation))
episode_return += reward
if done:
if info["success"]:
filename = "demonstration_%d.json" % (succesful_episodes + 1)
save_file(observations, cfg.output_dir, filename)
succesful_episodes += 1
break
print("Episode_return", episode_return, "Epsiode_length", t)
print("Total succesful episodes : %d/%d" % (succesful_episodes, cfg.number_demonstrations))
env.close()
def main(cfg):
# Do not show tacto renderer output
logger = logging.getLogger('tacto.renderer')
logger.propagate = False
for i in range(cfg.train.num_random_seeds):
# Training
env = custom_sawyer_peg_env(cfg.env)
gmm_model = GMM(str(Path(cfg.gmm_model).absolute()))
agent = SAC_GMM_Agent(env=env, model=gmm_model, **cfg.agent)
save_filename = get_save_filename("sac_gmm", cfg, i)
agent.train(**cfg.train.run, save_filename=save_filename)
agent.env.close()
# Testing
agent.env = custom_sawyer_peg_env(cfg.env)
agent.evaluate(**cfg.test.run)
agent.env.close()
def train(config, params=None):
#TODO: Save models
logger = logging.getLogger(__name__)
budget = 300
env = custom_sawyer_peg_env(params.env)
agent = SAC_Agent(env, **config)
agent.train(**params.train, num_episodes=int(budget))
accuracy, val_return, val_length = agent.evaluate(**params.validation)
agent.env.close()
logger.info("Final return reported to the optimizer: %2f" % val_return)
tune.report(val_return=val_return, val_accuracy=accuracy)
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):
# Do not show tacto renderer output
logger = logging.getLogger('tacto.renderer')
logger.propagate = False
for i in range(cfg.train.num_random_seeds):
# Training
env = custom_sawyer_peg_env(cfg.env)
agent = SAC_Agent(env, **cfg.agent)
save_filename = get_save_filename("sac_peg", cfg, i)
agent.train(**cfg.train.run, save_filename=save_filename)
agent.env.close()
def main(cfg):
# Do not show tacto renderer output
logger = logging.getLogger('tacto.renderer')
logger.propagate = False
env = custom_sawyer_peg_env(cfg.env)
gmm_model = GMM(add_cwd(cfg.gmm_model))
agent = SAC_GMM_Agent(env=env, model=gmm_model, **cfg.agent)
agent.load(add_cwd(cfg.test.model_name))
stats = agent.evaluate(**cfg.test.run)
logger = logging.getLogger(__name__)
logger.info(stats)
agent.env.close()
def main(cfg):
env = custom_sawyer_peg_env(cfg.env)
agent = SAC_Agent(env, cfg.agent)
agent.load(add_cwd(cfg.test.model_name))
min_seq = 5
force_sequences = {
"successes": {
"left": [],
"right": []
},
"failures": {
"left": [],
"right": []
}
}
successes, failures = 0, 0
while successes < min_seq or failures < min_seq:
force_readings = {"left": [], "right": []}
state = env.reset()
for step in range(env.max_episode_steps):
force_readings["left"].append(state[-2])
force_readings["right"].append(state[-1])
action = agent.getAction(state, deterministic=True)
next_state, reward, done, info = env.step(action)
state = next_state
if done:
break
if "success" in info and info['success']:
if successes < min_seq:
force_sequences["successes"]["left"].append(
force_readings["left"])
force_sequences["successes"]["right"].append(
force_readings["right"])
successes += 1
else:
if failures < min_seq:
force_sequences["failures"]["left"].append(
force_readings["left"])
force_sequences["failures"]["right"].append(
force_readings["right"])
failures += 1
plot_force_sequences(force_sequences)
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 compute(self, config, budget, working_directory, *args, **kwargs):
env = custom_sawyer_peg_env(self.cfg.env)
agent = SAC_Agent(env, **config)
self.logger.info("Starting agent with budget %d" % budget)
self.logger.info("Configuration: %s" % json.dumps(config))
save_dir = "models/iteration_%d" % self.iteration
self.logger.info("Save directory: %s" % save_dir)
save_filename = self.get_save_filename(self.cfg.env.observation)
agent.train(**self.cfg.train, num_episodes = int(budget), save_filename=save_filename, save_dir=save_dir)
accuracy, val_return, val_length = agent.evaluate(**self.cfg.validation)
self.logger.info("Final return reported to the optimizer: %2f" % val_return)
self.iteration += 1
agent.env.close()
# Release memory
del agent.replay_buffer
gc.collect()
return ({'loss': - val_return, # remember: HpBandSter always minimizes!
'info': { 'val_episode_length': val_length,
'accuracy': accuracy } })
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)