def test_nested_action_spaces(self):
config = DEFAULT_CONFIG.copy()
config["env"] = RandomEnv
# Write output to check, whether actions are written correctly.
tmp_dir = os.popen("mktemp -d").read()[:-1]
if not os.path.exists(tmp_dir):
# Last resort: Resolve via underlying tempdir (and cut tmp_.
tmp_dir = ray._private.utils.tempfile.gettempdir() + tmp_dir[4:]
assert os.path.exists(tmp_dir), f"'{tmp_dir}' not found!"
config["output"] = tmp_dir
# Switch off OPE as we don't write action-probs.
# TODO: We should probably always write those if `output` is given.
config["input_evaluation"] = []
# Pretend actions in offline files are already normalized.
config["actions_in_input_normalized"] = True
for _ in framework_iterator(config):
for name, action_space in SPACES.items():
config["env_config"] = {
"action_space": action_space,
}
for flatten in [False, True]:
print(f"A={action_space} flatten={flatten}")
shutil.rmtree(config["output"])
config["_disable_action_flattening"] = not flatten
trainer = PGTrainer(config)
trainer.train()
trainer.stop()
# Check actions in output file (whether properly flattened
# or not).
reader = JsonReader(
inputs=config["output"],
ioctx=trainer.workers.local_worker().io_context,
)
sample_batch = reader.next()
if flatten:
assert isinstance(sample_batch["actions"], np.ndarray)
assert len(sample_batch["actions"].shape) == 2
assert sample_batch["actions"].shape[0] == len(
sample_batch)
else:
tree.assert_same_structure(
trainer.get_policy().action_space_struct,
sample_batch["actions"],
)
# Test, whether offline data can be properly read by a
# BCTrainer, configured accordingly.
config["input"] = config["output"]
del config["output"]
bc_trainer = BCTrainer(config=config)
bc_trainer.train()
bc_trainer.stop()
config["output"] = tmp_dir
config["input"] = "sampler"
def test_itr_batches(self):
"""Test that the json reader iterates over batches of rows correctly."""
rllib_dir = Path(__file__).parent.parent.parent.parent
print("rllib dir={}".format(rllib_dir))
data_file = os.path.join(rllib_dir,
"rllib/tests/data/pendulum/large.json")
print("data_file={} exists={}".format(data_file,
os.path.isfile(data_file)))
ioctx = IOContext(config={"train_batch_size": 1200}, worker_index=0)
reader = JsonReader([data_file], ioctx)
assert len(reader.next()) == 1200
def _init(self, config, env_name):
self._policy_graph = self.config["multiagent"]["policy_graphs"]
self.local_evaluator = self.make_local_evaluator(
env_name, self._policy_graph, self.config)
self.remote_evaluators = self.make_remote_evaluators(
env_name, self._policy_graph, self.config["num_workers"])
self.train_batch_size = self.config["train_batch_size"]
self.num_sgd_iter = self.config["num_sgd_iter"]
self.num_train = self.config["num_train"]
self.expert_path = self.config["expert_path"]
self.theta_lr = self.config["theta_lr"]
expert_reader = JsonReader(self.expert_path)
self.expert_samples = expert_reader.next()
self.expert_features = self.calculate_expected_feature(self.expert_samples)
self.theta = np.random.uniform(size=self.expert_features.shape)
def offline_evaluation(self, iteration):
self._agent.eval_mode = True
validation_dataset = [
os.path.join(self.dataset_path, f)
for f in os.listdir(self.dataset_path)
if os.path.isfile(os.path.join(self.dataset_path, f))
]
validation_dataset = sorted(validation_dataset)
rewards = []
for n_eps in range(len(validation_dataset)):
reader = JsonReader(validation_dataset[n_eps])
with open(validation_dataset[n_eps], "r") as f:
sb = f.readlines()
for _ in range(len(sb)):
n = reader.next()
batch = reader.next()
for episode in batch.split_by_episode():
for r in episode["rewards"]:
rewards.append(r)
rewards_shift = (round(min(rewards), 5) *
-1 if round(min(rewards), 5) < 0 else round(
min(rewards), 5))
actions = []
estimation = {
"dm/score": [],
"dm/pred_reward_mean": [],
"dm/pred_reward_total": [],
"is/V_prev": [],
"is/V_step_IS": [],
"is/V_gain_est": [],
}
for n_eps in range(len(validation_dataset)):
reader = JsonReader(validation_dataset[n_eps])
batch = reader.next()
for episode in batch.split_by_episode():
action = []
selected_action_prob = []
all_actions_prob = []
for i in range(len(episode["eps_id"])):
_action, _action_prob = self._agent.step(
episode["rewards"][i], episode["obs"][i])
action.append(_action)
selected_action_prob.append(_action_prob[_action])
all_actions_prob.append(_action_prob)
is_estimation = self.is_estimator.estimate(
episode, all_actions_prob, rewards_shift)
actions.extend(action)
action = np.array([action])
action_prob = np.array([selected_action_prob])
obs = torch.Tensor(
np.concatenate(
(episode["obs"],
np.reshape(action, (action[0].shape[0], 1))),
axis=1,
)
) # concatenate actions and observations for input obs are usually [[obs1],[obs2],[obs3]] and
# actions are usually [1,0,1,0] so the goal is to make actions like this: [[1],[0],[1]]
scores_raw = self.predictor.predict(obs).detach().numpy()
scores = {}
scores["score"] = (scores_raw * action_prob).mean()
scores["pred_reward_mean"] = scores_raw.mean()
scores["pred_reward_total"] = scores_raw.sum()
# DM Estimation ------------------------
estimation["dm/score"].append(scores["score"])
estimation["dm/pred_reward_mean"].append(
scores["pred_reward_mean"])
estimation["dm/pred_reward_total"].append(
scores["pred_reward_total"])
# IS Estimation -----------------------
estimation["is/V_prev"].append(is_estimation["V_prev"])
estimation["is/V_step_IS"].append(is_estimation["V_step_IS"])
estimation["is/V_gain_est"].append(is_estimation["V_gain_est"])
est_mean = pd.DataFrame.from_dict(estimation).mean(axis=0)
summary = tf.Summary(value=[
tf.Summary.Value(tag="Eval/DM/score",
simple_value=est_mean["dm/score"]),
tf.Summary.Value(
tag="Eval/DM/pred_reward_mean",
simple_value=est_mean["dm/pred_reward_mean"],
),
tf.Summary.Value(
tag="Eval/DM/pred_reward_total",
simple_value=est_mean["dm/pred_reward_total"],
),
tf.Summary.Value(tag="Eval/is/V_prev",
simple_value=est_mean["is/V_prev"]),
tf.Summary.Value(
tag="Eval/is/V_step_IS",
simple_value=est_mean["is/V_step_IS"],
),
tf.Summary.Value(
tag="Eval/is/V_gain_est",
simple_value=est_mean["is/V_gain_est"],
),
tf.Summary.Value(
tag="Eval/actions_prob",
simple_value=float(actions.count(1)) / len(actions),
),
])
self._summary_writer.add_summary(summary, iteration)
def setUpClass(cls):
ray.init()
rllib_dir = Path(__file__).parent.parent.parent.parent
train_data = os.path.join(rllib_dir, "tests/data/cartpole/large.json")
eval_data = train_data
env_name = "CartPole-v0"
cls.gamma = 0.99
n_episodes = 40
cls.q_model_config = {"n_iters": 600}
config = (DQNConfig().environment(env=env_name).training(
gamma=cls.gamma).rollouts(
num_rollout_workers=3,
batch_mode="complete_episodes").framework("torch").resources(
num_gpus=int(os.environ.get("RLLIB_NUM_GPUS", 0))).
offline_data(input_=train_data).evaluation(
evaluation_interval=None,
evaluation_duration=n_episodes,
evaluation_num_workers=1,
evaluation_duration_unit="episodes",
evaluation_config={"input": eval_data},
off_policy_estimation_methods={
"is": {
"type": ImportanceSampling
},
"wis": {
"type": WeightedImportanceSampling
},
"dm_fqe": {
"type": DirectMethod,
"q_model_config": {
"type": FQETorchModel
},
},
"dr_fqe": {
"type": DoublyRobust,
"q_model_config": {
"type": FQETorchModel
},
},
},
))
cls.algo = config.build()
# Train DQN for evaluation policy
for _ in range(n_episodes):
cls.algo.train()
# Read n_episodes of data, assuming that one line is one episode
reader = JsonReader(eval_data)
cls.batch = reader.next()
for _ in range(n_episodes - 1):
cls.batch = concat_samples([cls.batch, reader.next()])
cls.n_episodes = len(cls.batch.split_by_episode())
print("Episodes:", cls.n_episodes, "Steps:", cls.batch.count)
cls.mean_ret = {}
cls.std_ret = {}
cls.losses = {}
# Simulate Monte-Carlo rollouts
mc_ret = []
env = gym.make(env_name)
for _ in range(n_episodes):
obs = env.reset()
done = False
rewards = []
while not done:
act = cls.algo.compute_single_action(obs)
obs, reward, done, _ = env.step(act)
rewards.append(reward)
ret = 0
for r in reversed(rewards):
ret = r + cls.gamma * ret
mc_ret.append(ret)
cls.mean_ret["simulation"] = np.mean(mc_ret)
cls.std_ret["simulation"] = np.std(mc_ret)
def setUpClass(cls):
ray.init(ignore_reinit_error=True)
rllib_dir = Path(__file__).parent.parent.parent.parent
print("rllib dir={}".format(rllib_dir))
data_file = os.path.join(rllib_dir, "tests/data/cartpole/large.json")
print("data_file={} exists={}".format(data_file, os.path.isfile(data_file)))
env_name = "CartPole-v0"
cls.gamma = 0.99
train_steps = 20000
n_batches = 20 # Approx. equal to n_episodes
n_eval_episodes = 100
config = (
DQNConfig()
.environment(env=env_name)
.training(gamma=cls.gamma)
.rollouts(num_rollout_workers=3)
.exploration(
explore=True,
exploration_config={
"type": "SoftQ",
"temperature": 1.0,
},
)
.framework("torch")
.rollouts(batch_mode="complete_episodes")
)
cls.trainer = config.build()
# Train DQN for evaluation policy
tune.run(
"DQN",
config=config.to_dict(),
stop={"timesteps_total": train_steps},
verbose=0,
)
# Read n_batches of data
reader = JsonReader(data_file)
cls.batch = reader.next()
for _ in range(n_batches - 1):
cls.batch = cls.batch.concat(reader.next())
cls.n_episodes = len(cls.batch.split_by_episode())
print("Episodes:", cls.n_episodes, "Steps:", cls.batch.count)
cls.mean_ret = {}
cls.std_ret = {}
# Simulate Monte-Carlo rollouts
mc_ret = []
env = gym.make(env_name)
for _ in range(n_eval_episodes):
obs = env.reset()
done = False
rewards = []
while not done:
act = cls.trainer.compute_single_action(obs)
obs, reward, done, _ = env.step(act)
rewards.append(reward)
ret = 0
for r in reversed(rewards):
ret = r + cls.gamma * ret
mc_ret.append(ret)
cls.mean_ret["simulation"] = np.mean(mc_ret)
cls.std_ret["simulation"] = np.std(mc_ret)
# Optional configs for the model-based estimators
cls.model_config = {"k": 2, "n_iters": 10}
ray.shutdown()