def test_create_df_from_replay_buffer(self):
env_name = "MiniGrid-Empty-5x5-v0"
env = Gym(env_name=env_name)
state_dim = env.observation_space.shape[0]
# Wrap env in TestEnv
env = TestEnv(env)
problem_domain = ProblemDomain.DISCRETE_ACTION
DATASET_SIZE = 1000
multi_steps = None
DS = "2021-09-16"
# Generate data
df = create_df_from_replay_buffer(
env=env,
problem_domain=problem_domain,
desired_size=DATASET_SIZE,
multi_steps=multi_steps,
ds=DS,
shuffle_df=False,
)
self.assertEqual(len(df), DATASET_SIZE)
# Check data
preprocessor = PythonSparseToDenseProcessor(list(range(state_dim)))
for idx, row in df.iterrows():
df_mdp_id = row["mdp_id"]
env_mdp_id = str(env.sart[idx][0])
self.assertEqual(df_mdp_id, env_mdp_id)
df_seq_num = row["sequence_number"]
env_seq_num = env.sart[idx][1]
self.assertEqual(df_seq_num, env_seq_num)
df_state = preprocessor.process([row["state_features"]
])[0][0].numpy()
env_state = env.sart[idx][2]
npt.assert_array_equal(df_state, env_state)
df_action = row["action"]
env_action = str(env.sart[idx][3])
self.assertEqual(df_action, env_action)
df_terminal = row["next_action"] == ""
env_terminal = env.sart[idx][5]
self.assertEqual(df_terminal, env_terminal)
if not df_terminal:
df_reward = float(row["reward"])
env_reward = float(env.sart[idx][4])
npt.assert_allclose(df_reward, env_reward)
df_next_state = preprocessor.process(
[row["next_state_features"]])[0][0].numpy()
env_next_state = env.sart[idx + 1][2]
npt.assert_array_equal(df_next_state, env_next_state)
df_next_action = row["next_action"]
env_next_action = str(env.sart[idx + 1][3])
self.assertEqual(df_next_action, env_next_action)
else:
del env.sart[idx + 1]
def test_int_key_sparse_to_dense(self):
# int keys, set_missing_value_to_zero=False
processor = PythonSparseToDenseProcessor(
self.sorted_features, set_missing_value_to_zero=False)
value, presence = processor.process(self.int_keyed_sparse_data)
assert torch.allclose(value, self.expected_value_missing)
assert torch.all(presence == self.expected_presence_missing)