def benchmark_trpo(self): # pylint: disable=no-self-use
"""Compare benchmarks between garage and baselines."""
mujoco1m = benchmarks.get_benchmark('Mujoco1M')
timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S-%f')
benchmark_dir = './data/local/benchmarks/trpo/%s/' % timestamp
result_json = {}
for task in mujoco1m['tasks']:
env_id = task['env_id']
env = gym.make(env_id)
seeds = random.sample(range(100), hyper_parameters['n_trials'])
task_dir = osp.join(benchmark_dir, env_id)
plt_file = osp.join(benchmark_dir,
'{}_benchmark.png'.format(env_id))
garage_tf_csvs = []
garage_pytorch_csvs = []
for trial in range(hyper_parameters['n_trials']):
_PLACEHOLDER_CACHE.clear()
seed = seeds[trial]
trial_dir = task_dir + '/trial_%d_seed_%d' % (trial + 1, seed)
garage_tf_dir = trial_dir + '/garage_tf'
garage_pytorch_dir = trial_dir + '/garage_pytorch'
# Run garage algorithms
env.reset()
garage_pytorch_csv = run_garage_pytorch(
env, seed, garage_pytorch_dir)
# pylint: disable=not-context-manager
with tf.Graph().as_default():
env.reset()
garage_tf_csv = run_garage_tf(env, seed, garage_tf_dir)
garage_tf_csvs.append(garage_tf_csv)
garage_pytorch_csvs.append(garage_pytorch_csv)
env.close()
benchmark_helper.plot_average_over_trials(
[garage_tf_csvs, garage_pytorch_csvs],
['Evaluation/AverageReturn'] * 2,
plt_file=plt_file,
env_id=env_id,
x_label='Iteration',
y_label='AverageReturn',
names=['garage-TensorFlow', 'garage-PyTorch'],
)
result_json[env_id] = benchmark_helper.create_json(
[garage_tf_csvs, garage_pytorch_csvs],
seeds=seeds,
trials=hyper_parameters['n_trials'],
xs=['Evaluation/Iteration'] * 2,
ys=['Evaluation/AverageReturn'] * 2,
factors=[hyper_parameters['batch_size']] * 2,
names=['garage-TF', 'garage-PT'])
Rh.write_file(result_json, 'TRPO')
def test_benchmark_trpo(self):
"""
Compare benchmarks between garage and baselines.
:return:
"""
mujoco1m = benchmarks.get_benchmark("Mujoco1M")
timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S-%f")
benchmark_dir = "./data/local/benchmarks/trpo/%s/" % timestamp
for task in mujoco1m["tasks"]:
env_id = task["env_id"]
env = gym.make(env_id)
baseline_env = AutoStopEnv(env_name=env_id, max_path_length=100)
seeds = random.sample(range(100), task["trials"])
task_dir = osp.join(benchmark_dir, env_id)
plt_file = osp.join(benchmark_dir,
"{}_benchmark.png".format(env_id))
baselines_csvs = []
garage_csvs = []
for trial in range(task["trials"]):
_PLACEHOLDER_CACHE.clear()
seed = seeds[trial]
trial_dir = task_dir + "/trial_%d_seed_%d" % (trial + 1, seed)
garage_dir = trial_dir + "/garage"
baselines_dir = trial_dir + "/baselines"
with tf.Graph().as_default():
# Run garage algorithms
env.reset()
garage_csv = run_garage(env, seed, garage_dir)
# Run baseline algorithms
baseline_env.reset()
baselines_csv = run_baselines(baseline_env, seed,
baselines_dir)
garage_csvs.append(garage_csv)
baselines_csvs.append(baselines_csv)
plot(b_csvs=baselines_csvs,
g_csvs=garage_csvs,
g_x="Iteration",
g_y="EpisodeRewardMean",
b_x="EpThisIter",
b_y="EpRewMean",
trials=task["trials"],
seeds=seeds,
plt_file=plt_file,
env_id=env_id)
env.close()
def test_benchmark_trpo(self):
"""
Compare benchmarks between garage and baselines.
:return:
"""
mujoco1m = benchmarks.get_benchmark("Mujoco1M")
timestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S-%f")
benchmark_dir = "./benchmark_trpo/%s/" % timestamp
for task in mujoco1m["tasks"]:
env_id = task["env_id"]
env = gym.make(env_id)
seeds = random.sample(range(100), task["trials"])
task_dir = osp.join(benchmark_dir, env_id)
plt_file = osp.join(benchmark_dir,
"{}_benchmark.png".format(env_id))
baselines_csvs = []
garage_csvs = []
for trail in range(task["trials"]):
_PLACEHOLDER_CACHE.clear()
env.reset()
seed = seeds[trail]
trail_dir = task_dir + "/trail_%d_seed_%d" % (trail + 1, seed)
garage_dir = trail_dir + "/garage"
baselines_dir = trail_dir + "/baselines"
baselines_csv = run_baselines(env_id, seed, baselines_dir)
# Run garage algorithms
env.reset()
garage_csv = run_garage(env, seed, garage_dir)
garage_csvs.append(garage_csv)
baselines_csvs.append(baselines_csv)
plot(b_csvs=baselines_csvs,
g_csvs=garage_csvs,
g_x="Iteration",
g_y="AverageReturn",
b_x="Iter",
b_y="EpRewMean",
trails=task["trials"],
seeds=seeds,
plt_file=plt_file,
env_id=env_id)
def test_benchmark_trpo(self):
'''
Compare benchmarks between garage and baselines.
:return:
'''
mujoco1m = benchmarks.get_benchmark('Mujoco1M')
timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S-%f')
benchmark_dir = './data/local/benchmarks/trpo/%s/' % timestamp
result_json = {}
for task in mujoco1m['tasks']:
env_id = task['env_id']
env = gym.make(env_id)
baseline_env = AutoStopEnv(env_name=env_id, max_path_length=100)
seeds = random.sample(range(100), task['trials'])
task_dir = osp.join(benchmark_dir, env_id)
plt_file = osp.join(benchmark_dir,
'{}_benchmark.png'.format(env_id))
baselines_csvs = []
garage_csvs = []
for trial in range(task['trials']):
_PLACEHOLDER_CACHE.clear()
seed = seeds[trial]
trial_dir = task_dir + '/trial_%d_seed_%d' % (trial + 1, seed)
garage_dir = trial_dir + '/garage'
baselines_dir = trial_dir + '/baselines'
with tf.Graph().as_default():
# Run garage algorithms
env.reset()
garage_csv = run_garage(env, seed, garage_dir)
# Run baseline algorithms
baseline_env.reset()
baselines_csv = run_baselines(baseline_env, seed,
baselines_dir)
garage_csvs.append(garage_csv)
baselines_csvs.append(baselines_csv)
Rh.plot(b_csvs=baselines_csvs,
g_csvs=garage_csvs,
g_x='Iteration',
g_y='AverageReturn',
b_x='EpThisIter',
b_y='EpRewMean',
trials=task['trials'],
seeds=seeds,
plt_file=plt_file,
env_id=env_id,
x_label='Iteration',
y_label='AverageReturn')
result_json[env_id] = Rh.create_json(b_csvs=baselines_csvs,
g_csvs=garage_csvs,
seeds=seeds,
trails=task['trials'],
g_x='Iteration',
g_y='AverageReturn',
b_x='TimestepsSoFar',
b_y='EpRewMean',
factor_g=1024,
factor_b=1)
env.close()
Rh.write_file(result_json, 'TRPO')
