class TestSimulator(TestCase):
def setUp(self) -> None:
traj_file = Path("/home/test/test.csv")
self.batch_size = 32
self.simulator = ParallelSimulator(traj_file,
num_replicas=self.batch_size,
max_state_len=5,
max_traj_len=20)
self.simulator.seed(1)
def test_reset(self):
obs = self.simulator.reset()
self.assertIsInstance(obs[0], ReturnStateTuple)
self.assertEqual(len(obs), self.batch_size)
def test_step(self):
self.simulator.reset()
action = np.random.randint(1, 100, size=(self.batch_size, 5))
next_state, done = self.simulator.step(action)
self.assertEqual(len(next_state), self.batch_size)
def test_multiple_steps(self):
self.simulator.reset()
action = np.random.randint(1, 100, size=(self.batch_size, 5))
self.simulator.step(action)
self.simulator.step(action)
self.simulator.step(action)
self.simulator.step(action)
self.simulator.step(action)
s = self.simulator.step(action)
def test_run_to_end(self):
obs = self.simulator.reset()
action = np.random.randint(1, 100, size=(self.batch_size, 5))
while len(obs) > 0:
obs, _ = self.simulator.step(action)
class TestSimulator(TestCase):
def setUp(self) -> None:
data_dir = Path("/home/alex/workspace/datasets/ml/ml-1m")
self.batch_size = 1
self.simulator = ParallelSimulator(data_dir / "test.csv",
simulation_type=data_dir /
"simulator/bpr/batch-rl-test/0",
num_replicas=self.batch_size,
max_state_len=10,
variant="bpr",
reward_type="item")
self.simulator.seed(1)
def test_reset(self):
obs = self.simulator.reset()
self.assertEqual(len(obs), self.batch_size)
def test_step(self):
self.simulator.reset()
action = np.random.randint(1, 3000, size=(self.batch_size, 10))
next_state, rewards, done, info = self.simulator.step(action)
self.assertEqual(len(next_state), self.batch_size)
def test_multiple_steps(self):
self.simulator.reset()
action = np.random.randint(1, 100, size=(self.batch_size, 5))
done = True
while not done:
next_state, rewards, done, info = self.simulator.step(action)
def test_break_soon(self):
episode_lens = []
rewardsep = []
while True:
obs = self.simulator.reset()
if obs is None:
break
done = False
e = 0
action = np.random.randint(1, 30, size=(len(obs), 10))
r = 0
while not done:
next_state, rewards, done, info = self.simulator.step(action)
r += rewards
e += 1
episode_lens.append(e)
rewardsep.append(r)
print("repeated")
print(np.mean(episode_lens), np.std(episode_lens))
print(np.mean(rewardsep), np.std(rewardsep))
def test_run_to_end_with_much_random(self):
episode_lens = []
rewardsep = []
while True:
obs = self.simulator.reset()
if obs is None:
break
done = False
e = 0
while not done:
action = np.random.randint(1, 3000, size=(len(obs), 10))
obs, rewards, done, info = self.simulator.step(action)
rewardsep.append(rewards.mean())
e += 1
episode_lens.append(e)
print("random")
print(np.mean(episode_lens), np.std(episode_lens))
print(np.mean(rewardsep), np.std(rewardsep))
def test_run_to_end_tricky(self):
episode_lens = []
rewardsep = []
action = np.arange(1, 101).reshape(10, 10)
while True:
obs = self.simulator.reset()
if obs is None:
break
done = False
e = 0
while not done:
a = np.expand_dims(action[e % 10], 0)
a = np.repeat(a, len(obs), axis=0)
obs, rewards, done, info = self.simulator.step(a)
rewardsep.append(rewards.mean())
e += 1
episode_lens.append(e)
print("tricky")
print(np.mean(episode_lens), np.std(episode_lens))
print(np.mean(rewardsep), np.std(rewardsep))