def test_evaluation(self):
"""tests the evaluation method of the pointrobot trainer"""
trainer = PointrobotTrainer(self.policy,
self.env,
self.params,
test_env=self.test_env)
trainer.evaluate()
def test_training(self):
"""sanity check of the training method."""
self.params["trainer"]["max_steps"] = 1e4
trainer = PointrobotTrainer(self.policy,
self.env,
self.params,
test_env=self.test_env)
trainer.train()
# getting random hyperparams according to the ranges and placing them into params.
params = get_random_params(params)
# setting up logdir for the current hyperparams:
logdir = os.path.join('results', 'hyperparam_tuning', str(run))
os.makedirs(logdir)
params["trainer"]["logdir"] = logdir
# write the actual hyperparams into a file:
info_file = os.path.join(logdir, "params.txt")
export_params(params, info_file)
#Initialize the environment
env = gym.make(
params["env"]["name"],
params=params,
)
test_env = gym.make(params["env"]["name"], params=params)
# deleting the previous checkpoints:
ckp_files = glob.glob(os.path.join(params["trainer"]["model_dir"], "*"))
for f in ckp_files:
os.remove(f)
# initialize the agent:
policy = DDPG(env=env, params=params)
# initialize the trainer:
trainer = PointrobotTrainer(policy, env, params, test_env=test_env)
trainer.train()
def test_pointrobot_trainer_init(self):
"""tests the __init__() function of the pointrobot trainer"""
trainer = PointrobotTrainer(self.policy,
self.env,
self.params,
test_env=self.test_env)
params = set_up_benchmark_params(params, key)
params["trainer"]["logdir"] = os.path.join(params["trainer"]["logdir"],
key)
#Initialize the environment
env = gym.make(
params["env"]["name"],
params=params,
)
test_env = gym.make(params["env"]["name"], params=params)
# initialize the agent:
policy = DDPG(env=env, params=params)
# initialize the trainer:
trainer = PointrobotTrainer(policy, env, params, test_env=test_env)
avg_test_return, success_rate, ratio_straight_lines, success_rate_straight_line, success_rate_no_straight_line = trainer.evaluate(
)
# writing the results into a log file.
results_path = os.path.join(params["trainer"]["logdir"], "results.txt")
with open(results_path, 'a') as f:
f.write("model_dir: {}".format(params["trainer"]["model_dir"]) + "\n")
f.write("WS_level: {}".format(params["env"]["WS_level"]) + "\n")
f.write("num_obj_max: {}".format(params["env"]["num_obj_max"]) + "\n")
f.write("avg_test_return: {}".format(avg_test_return) + "\n")
f.write("success_rate: {}".format(success_rate) + "\n")
f.write("ratio_straight_lines: {}".format(ratio_straight_lines) + "\n")
f.write("success_rate_straight_line: {}".format(
success_rate_straight_line) + "\n")