def test_polynomial_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 23, 1000]
expected = [
0.5 + (2.0 - 0.5) * (1.0 - min(t, 100) / 100)**2 for t in ts
]
config = dict(
type=
"ray.rllib.utils.schedules.polynomial_schedule.PolynomialSchedule",
schedule_timesteps=100,
initial_p=2.0,
final_p=0.5,
power=2.0,
)
for fw in framework_iterator(
frameworks=["tf2", "tf", "tfe", "torch", None]):
polynomial = from_config(config, framework=fw)
for t, e in zip(ts, expected):
out = polynomial(t)
check(out, e, decimals=4)
ts_as_tensors = self._get_framework_tensors(ts, fw)
for t, e in zip(ts_as_tensors, expected):
out = polynomial(t)
assert fw != "tf" or isinstance(out, tf.Tensor)
check(out, e, decimals=4)
def test_constant_schedule(self):
value = 2.3
ts = [100, 0, 10, 2, 3, 4, 99, 56, 10000, 23, 234, 56]
for fw in ["tf", "torch", None]:
constant = from_config(ConstantSchedule,
dict(value=value, framework=fw))
for t in ts:
out = constant(t)
check(out, value)
def test_linear_schedule(self):
ts = [0, 50, 10, 100, 90, 2, 1, 99, 23, 1000]
config = {"schedule_timesteps": 100, "initial_p": 2.1, "final_p": 0.6}
for fw in framework_iterator(frameworks=["tf", "tfe", "torch", None]):
fw_ = fw if fw != "tfe" else "tf"
linear = from_config(LinearSchedule, config, framework=fw_)
for t in ts:
out = linear(t)
check(out, 2.1 - (min(t, 100) / 100) * (2.1 - 0.6), decimals=4)
def test_piecewise_schedule(self):
piecewise = from_config(
PiecewiseSchedule,
dict(endpoints=[(0, 50.0), (25, 100.0), (30, 200.0)],
outside_value=14.5))
ts = [0, 5, 10, 100, 90, 2, 1, 99, 27]
expected = [50.0, 60.0, 70.0, 14.5, 14.5, 54.0, 52.0, 14.5, 140.0]
for t, e in zip(ts, expected):
out = piecewise(t)
check(out, e, decimals=4)
def test_exponential_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
config = dict(initial_p=2.0, decay_rate=0.99, schedule_timesteps=100)
for fw in framework_iterator(frameworks=["tf", "tfe", "torch", None]):
fw_ = fw if fw != "tfe" else "tf"
exponential = from_config(
ExponentialSchedule, config, framework=fw_)
for t in ts:
out = exponential(t)
check(out, 2.0 * 0.99**(t / 100), decimals=4)
def test_piecewise_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 27]
expected = [50.0, 60.0, 70.0, 14.5, 14.5, 54.0, 52.0, 14.5, 140.0]
config = dict(endpoints=[(0, 50.0), (25, 100.0), (30, 200.0)],
outside_value=14.5)
for fw in framework_iterator(frameworks=["tf", "tfe", "torch", None]):
fw_ = fw if fw != "tfe" else "tf"
piecewise = from_config(PiecewiseSchedule, config, framework=fw_)
for t, e in zip(ts, expected):
out = piecewise(t)
check(out, e, decimals=4)
def test_constant_schedule(self):
value = 2.3
ts = [100, 0, 10, 2, 3, 4, 99, 56, 10000, 23, 234, 56]
config = {"value": value}
for fw in framework_iterator(frameworks=["tf", "tfe", "torch", None]):
fw_ = fw if fw != "tfe" else "tf"
constant = from_config(ConstantSchedule, config, framework=fw_)
for t in ts:
out = constant(t)
check(out, value)
def test_exponential_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
for fw in ["tf", "torch", None]:
exponential = from_config(
ExponentialSchedule,
dict(initial_p=2.0,
decay_rate=0.99,
schedule_timesteps=100,
framework=fw))
for t in ts:
out = exponential(t)
check(out, 2.0 * 0.99**(t / 100), decimals=4)
def test_linear_schedule(self):
ts = [0, 50, 10, 100, 90, 2, 1, 99, 23]
for fw in ["tf", "torch", None]:
linear = from_config(
LinearSchedule, {
"schedule_timesteps": 100,
"initial_p": 2.1,
"final_p": 0.6,
"framework": fw
})
if fw == "tf":
tf.enable_eager_execution()
for t in ts:
out = linear(t)
check(out, 2.1 - (t / 100) * (2.1 - 0.6), decimals=4)
def test_linear_schedule(self):
ts = [0, 50, 10, 100, 90, 2, 1, 99, 23]
config = {"schedule_timesteps": 100, "initial_p": 2.1, "final_p": 0.6}
for fw in ["tf", "torch", None]:
linear = from_config(LinearSchedule, config, framework=fw)
for t in ts:
out = linear(t)
check(out, 2.1 - (t / 100) * (2.1 - 0.6), decimals=4)
# Test eager as well.
with eager_mode():
linear = from_config(LinearSchedule, config, framework="tf")
for t in ts:
out = linear(t)
check(out, 2.1 - (t / 100) * (2.1 - 0.6), decimals=4)
def test_polynomial_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 23, 1000]
config = dict(type="ray.rllib.utils.schedules.polynomial_schedule."
"PolynomialSchedule",
schedule_timesteps=100,
initial_p=2.0,
final_p=0.5,
power=2.0)
for fw in framework_iterator(frameworks=["tf", "tfe", "torch", None]):
fw_ = fw if fw != "tfe" else "tf"
polynomial = from_config(config, framework=fw_)
for t in ts:
out = polynomial(t)
t = min(t, 100)
check(out, 0.5 + (2.0 - 0.5) * (1.0 - t / 100)**2, decimals=4)
def test_polynomial_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
for fw in ["tf", "torch", None]:
polynomial = from_config(
dict(type="ray.rllib.utils.schedules.polynomial_schedule."
"PolynomialSchedule",
schedule_timesteps=100,
initial_p=2.0,
final_p=0.5,
power=2.0,
framework=fw))
if fw == "tf":
tf.enable_eager_execution()
for t in ts:
out = polynomial(t)
check(out, 0.5 + (2.0 - 0.5) * (1.0 - t / 100)**2, decimals=4)
def test_linear_schedule(self):
ts = [0, 50, 10, 100, 90, 2, 1, 99, 23, 1000]
expected = [2.1 - (min(t, 100) / 100) * (2.1 - 0.6) for t in ts]
config = {"schedule_timesteps": 100, "initial_p": 2.1, "final_p": 0.6}
for fw in framework_iterator(
frameworks=["tf2", "tf", "tfe", "torch", None]):
linear = from_config(LinearSchedule, config, framework=fw)
for t, e in zip(ts, expected):
out = linear(t)
check(out, e, decimals=4)
ts_as_tensors = self._get_framework_tensors(ts, fw)
for t, e in zip(ts_as_tensors, expected):
out = linear(t)
assert fw != "tf" or isinstance(out, tf.Tensor)
check(out, e, decimals=4)
def test_exponential_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
config = dict(initial_p=2.0, decay_rate=0.99, schedule_timesteps=100)
for fw in ["tf", "torch", None]:
config["framework"] = fw
exponential = from_config(ExponentialSchedule, config)
for t in ts:
out = exponential(t)
check(out, 2.0 * 0.99**(t / 100), decimals=4)
# Test eager as well.
with eager_mode():
config["framework"] = "tf"
exponential = from_config(ExponentialSchedule, config)
for t in ts:
out = exponential(t)
check(out, 2.0 * 0.99**(t / 100), decimals=4)
def test_constant_schedule(self):
value = 2.3
ts = [100, 0, 10, 2, 3, 4, 99, 56, 10000, 23, 234, 56]
config = {"value": value}
for fw in ["tf", "torch", None]:
constant = from_config(ConstantSchedule, config, framework=fw)
for t in ts:
out = constant(t)
check(out, value)
# Test eager as well.
with eager_mode():
constant = from_config(ConstantSchedule, config, framework="tf")
for t in ts:
out = constant(t)
check(out, value)
def test_constant_schedule(self):
value = 2.3
ts = [100, 0, 10, 2, 3, 4, 99, 56, 10000, 23, 234, 56]
config = {"value": value}
for fw in framework_iterator(
frameworks=["tf2", "tf", "tfe", "torch", None]):
constant = from_config(ConstantSchedule, config, framework=fw)
for t in ts:
out = constant(t)
check(out, value)
ts_as_tensors = self._get_framework_tensors(ts, fw)
for t in ts_as_tensors:
out = constant(t)
assert fw != "tf" or isinstance(out, tf.Tensor)
check(out, value, decimals=4)
def test_piecewise_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 27]
expected = [50.0, 60.0, 70.0, 14.5, 14.5, 54.0, 52.0, 14.5, 140.0]
config = dict(endpoints=[(0, 50.0), (25, 100.0), (30, 200.0)],
outside_value=14.5)
for fw in framework_iterator(
frameworks=["tf2", "tf", "tfe", "torch", None]):
piecewise = from_config(PiecewiseSchedule, config, framework=fw)
for t, e in zip(ts, expected):
out = piecewise(t)
check(out, e, decimals=4)
ts_as_tensors = self._get_framework_tensors(ts, fw)
for t, e in zip(ts_as_tensors, expected):
out = piecewise(t)
assert fw != "tf" or isinstance(out, tf.Tensor)
check(out, e, decimals=4)
def test_piecewise_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 27]
expected = [50.0, 60.0, 70.0, 14.5, 14.5, 54.0, 52.0, 14.5, 140.0]
config = dict(endpoints=[(0, 50.0), (25, 100.0), (30, 200.0)],
outside_value=14.5)
for fw in ["tf", "torch", None]:
config["framework"] = fw
piecewise = from_config(PiecewiseSchedule, config)
for t, e in zip(ts, expected):
out = piecewise(t)
check(out, e, decimals=4)
# Test eager as well.
with eager_mode():
config["framework"] = "tf"
piecewise = from_config(PiecewiseSchedule, config)
for t, e in zip(ts, expected):
out = piecewise(t)
check(out, e, decimals=4)
def do_test_explorations(
run, env, config, dummy_obs, prev_a=None, expected_mean_action=None
):
"""Calls an Agent's `compute_actions` with different `explore` options."""
core_config = config.copy()
if run not in [a3c.A3CTrainer]:
core_config["num_workers"] = 0
# Test all frameworks.
for _ in framework_iterator(core_config):
print("Agent={}".format(run))
# Test for both the default Agent's exploration AND the `Random`
# exploration class.
for exploration in [None, "Random"]:
local_config = core_config.copy()
if exploration == "Random":
# TODO(sven): Random doesn't work for IMPALA yet.
if run is impala.ImpalaTrainer:
continue
local_config["exploration_config"] = {"type": "Random"}
print("exploration={}".format(exploration or "default"))
trainer = run(config=local_config, env=env)
# Make sure all actions drawn are the same, given same
# observations.
actions = []
for _ in range(25):
actions.append(
trainer.compute_single_action(
observation=dummy_obs,
explore=False,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None,
)
)
check(actions[-1], actions[0])
# Make sure actions drawn are different
# (around some mean value), given constant observations.
actions = []
for _ in range(500):
actions.append(
trainer.compute_single_action(
observation=dummy_obs,
explore=True,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None,
)
)
check(
np.mean(actions),
expected_mean_action if expected_mean_action is not None else 0.5,
atol=0.4,
)
# Check that the stddev is not 0.0 (values differ).
check(np.std(actions), 0.0, false=True)
def test_polynomial_schedule(self):
ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
config = dict(type="ray.rllib.utils.schedules.polynomial_schedule."
"PolynomialSchedule",
schedule_timesteps=100,
initial_p=2.0,
final_p=0.5,
power=2.0)
for fw in ["tf", "torch", None]:
config["framework"] = fw
polynomial = from_config(config)
for t in ts:
out = polynomial(t)
check(out, 0.5 + (2.0 - 0.5) * (1.0 - t / 100)**2, decimals=4)
# Test eager as well.
with eager_mode():
config["framework"] = "tf"
polynomial = from_config(config)
for t in ts:
out = polynomial(t)
check(out, 0.5 + (2.0 - 0.5) * (1.0 - t / 100)**2, decimals=4)
def test_explorations(run,
env,
config,
dummy_obs,
prev_a=None,
expected_mean_action=None):
"""Calls an Agent's `compute_actions` with different `explore` options."""
config = config.copy()
if run not in [a3c.A3CTrainer]:
config["num_workers"] = 0
# Test all frameworks.
for fw in ["torch", "eager", "tf"]:
if fw == "torch" and \
run in [dqn.DQNTrainer, dqn.SimpleQTrainer,
impala.ImpalaTrainer, sac.SACTrainer]:
continue
print("Testing {} in framework={}".format(run, fw))
config["eager"] = True if fw == "eager" else False
config["use_pytorch"] = True if fw == "torch" else False
# Test for both the default Agent's exploration AND the `Random`
# exploration class.
for exploration in [None]: # , "Random"]:
if exploration == "Random":
config["exploration_config"] = {"type": "Random"}
trainer = run(config=config, env=env)
# Make sure all actions drawn are the same, given same
# observations.
actions = []
for _ in range(100):
actions.append(
trainer.compute_action(
observation=dummy_obs,
explore=False,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None))
check(actions[-1], actions[0])
# Make sure actions drawn are different (around some mean value),
# given constant observations.
actions = []
for _ in range(100):
actions.append(
trainer.compute_action(
observation=dummy_obs,
explore=True,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None))
check(
np.mean(actions),
expected_mean_action
if expected_mean_action is not None else 0.5,
atol=0.3)
# Check that the stddev is not 0.0 (values differ).
check(np.std(actions), 0.0, false=True)
def test_exponential_schedule(self):
decay_rate = 0.2
ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
expected = [2.0 * decay_rate**(t / 100) for t in ts]
config = dict(initial_p=2.0,
decay_rate=decay_rate,
schedule_timesteps=100)
for fw in framework_iterator(
frameworks=["tf2", "tf", "tfe", "torch", None]):
exponential = from_config(ExponentialSchedule,
config,
framework=fw)
for t, e in zip(ts, expected):
out = exponential(t)
check(out, e, decimals=4)
ts_as_tensors = self._get_framework_tensors(ts, fw)
for t, e in zip(ts_as_tensors, expected):
out = exponential(t)
assert fw != "tf" or isinstance(out, tf.Tensor)
check(out, e, decimals=4)
def test_no_curiosity(self):
config = ppo.DEFAULT_CONFIG
env = "CartPole-v0"
dummy_obs = np.array([0.0, 0.1, 0.0, 0.0])
prev_a = np.array(0)
config["framework"] = "torch"
config["exploration_config"] = {"type": "ParameterNoise"}
trainer = ppo.PPOTrainer(config=config, env=env)
trainer.train()
# Make sure all actions drawn are the same, given same
# observations. Tests the explorations API.
actions = []
for _ in range(5):
actions.append(
trainer.compute_action(
observation=dummy_obs,
explore=False,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None))
check(actions[-1], actions[0])
print(actions)
def test_pg_loss_functions(self):
"""Tests the PG loss function math."""
config = pg.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
config["eager"] = True
config["gamma"] = 0.99
config["model"]["fcnet_hiddens"] = [10]
config["model"]["fcnet_activation"] = "linear"
# Fake CartPole episode of n time steps.
train_batch = {
SampleBatch.CUR_OBS:
np.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8],
[0.9, 1.0, 1.1, 1.2]]),
SampleBatch.ACTIONS:
np.array([0, 1, 1]),
SampleBatch.REWARDS:
np.array([1.0, 1.0, 1.0]),
SampleBatch.DONES:
np.array([False, False, True])
}
# tf.
trainer = pg.PGTrainer(config=config, env="CartPole-v0")
policy = trainer.get_policy()
vars = policy.model.trainable_variables()
# Post-process (calculate simple (non-GAE) advantages) and attach to
# train_batch dict.
# A = [0.99^2 * 1.0 + 0.99 * 1.0 + 1.0, 0.99 * 1.0 + 1.0, 1.0] =
# [2.9701, 1.99, 1.0]
train_batch = pg.post_process_advantages(policy, train_batch)
# Check Advantage values.
check(train_batch[Postprocessing.ADVANTAGES], [2.9701, 1.99, 1.0])
# Actual loss results.
results = pg.pg_tf_loss(policy,
policy.model,
dist_class=Categorical,
train_batch=train_batch)
# Calculate expected results.
expected_logits = fc(
fc(train_batch[SampleBatch.CUR_OBS], vars[0].numpy(),
vars[1].numpy()), vars[2].numpy(), vars[3].numpy())
expected_logp = Categorical(expected_logits, policy.model).logp(
train_batch[SampleBatch.ACTIONS])
expected_loss = -np.mean(
expected_logp * train_batch[Postprocessing.ADVANTAGES])
check(results.numpy(), expected_loss, decimals=4)
# Torch.
config["use_pytorch"] = True
trainer = pg.PGTrainer(config=config, env="CartPole-v0")
policy = trainer.get_policy()
train_batch = policy._lazy_tensor_dict(train_batch)
results = pg.pg_torch_loss(policy,
policy.model,
dist_class=TorchCategorical,
train_batch=train_batch)
expected_logits = policy.model.last_output()
expected_logp = TorchCategorical(expected_logits, policy.model).logp(
train_batch[SampleBatch.ACTIONS])
expected_loss = -np.mean(
expected_logp.detach().numpy() *
train_batch[Postprocessing.ADVANTAGES].numpy())
check(results.detach().numpy(), expected_loss, decimals=4)
def do_test_explorations(run,
env,
config,
dummy_obs,
prev_a=None,
expected_mean_action=None):
"""Calls an Agent's `compute_actions` with different `explore` options."""
config = config.copy()
if run not in [a3c.A3CTrainer]:
config["num_workers"] = 0
# Test all frameworks.
for fw in ["tf", "eager", "torch"]:
if fw == "torch" and \
run in [ddpg.DDPGTrainer, dqn.DQNTrainer, dqn.SimpleQTrainer,
impala.ImpalaTrainer, sac.SACTrainer, td3.TD3Trainer]:
continue
elif fw == "eager" and run in [ddpg.DDPGTrainer, td3.TD3Trainer]:
continue
print("Testing {} in framework={}".format(run, fw))
config["eager"] = fw == "eager"
config["use_pytorch"] = fw == "torch"
# Test for both the default Agent's exploration AND the `Random`
# exploration class.
for exploration in [None, "Random"]:
if exploration == "Random":
# TODO(sven): Random doesn't work for IMPALA yet.
if run is impala.ImpalaTrainer:
continue
config["exploration_config"] = {"type": "Random"}
print("exploration={}".format(exploration or "default"))
eager_ctx = None
if fw == "eager":
eager_ctx = eager_mode()
eager_ctx.__enter__()
assert tf.executing_eagerly()
elif fw == "tf":
assert not tf.executing_eagerly()
trainer = run(config=config, env=env)
# Make sure all actions drawn are the same, given same
# observations.
actions = []
for _ in range(50):
actions.append(
trainer.compute_action(
observation=dummy_obs,
explore=False,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None))
check(actions[-1], actions[0])
# Make sure actions drawn are different
# (around some mean value), given constant observations.
actions = []
for _ in range(100):
actions.append(
trainer.compute_action(
observation=dummy_obs,
explore=True,
prev_action=prev_a,
prev_reward=1.0 if prev_a is not None else None))
check(
np.mean(actions),
expected_mean_action
if expected_mean_action is not None else 0.5,
atol=0.3)
# Check that the stddev is not 0.0 (values differ).
check(np.std(actions), 0.0, false=True)
if eager_ctx:
eager_ctx.__exit__(None, None, None)
def test_pg_loss_functions(self):
"""Tests the PG loss function math."""
config = pg.DEFAULT_CONFIG.copy()
config["num_workers"] = 0 # Run locally.
config["gamma"] = 0.99
config["model"]["fcnet_hiddens"] = [10]
config["model"]["fcnet_activation"] = "linear"
# Fake CartPole episode of n time steps.
train_batch = SampleBatch({
SampleBatch.OBS:
np.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8],
[0.9, 1.0, 1.1, 1.2]]),
SampleBatch.ACTIONS:
np.array([0, 1, 1]),
SampleBatch.REWARDS:
np.array([1.0, 1.0, 1.0]),
SampleBatch.DONES:
np.array([False, False, True]),
SampleBatch.EPS_ID:
np.array([1234, 1234, 1234]),
SampleBatch.AGENT_INDEX:
np.array([0, 0, 0]),
})
for fw, sess in framework_iterator(config, session=True):
dist_cls = (Categorical if fw != "torch" else TorchCategorical)
trainer = pg.PGTrainer(config=config, env="CartPole-v0")
policy = trainer.get_policy()
vars = policy.model.trainable_variables()
if sess:
vars = policy.get_session().run(vars)
# Post-process (calculate simple (non-GAE) advantages) and attach
# to train_batch dict.
# A = [0.99^2 * 1.0 + 0.99 * 1.0 + 1.0, 0.99 * 1.0 + 1.0, 1.0] =
# [2.9701, 1.99, 1.0]
train_batch_ = pg.post_process_advantages(policy,
train_batch.copy())
if fw == "torch":
train_batch_ = policy._lazy_tensor_dict(train_batch_)
# Check Advantage values.
check(train_batch_[Postprocessing.ADVANTAGES], [2.9701, 1.99, 1.0])
# Actual loss results.
if sess:
results = policy.get_session().run(
policy._loss,
feed_dict=policy._get_loss_inputs_dict(train_batch_,
shuffle=False))
else:
results = (pg.pg_tf_loss if fw in ["tf2", "tfe"] else
pg.pg_torch_loss)(policy,
policy.model,
dist_class=dist_cls,
train_batch=train_batch_)
# Calculate expected results.
if fw != "torch":
expected_logits = fc(fc(train_batch_[SampleBatch.OBS],
vars[0],
vars[1],
framework=fw),
vars[2],
vars[3],
framework=fw)
else:
expected_logits = fc(fc(train_batch_[SampleBatch.OBS],
vars[2],
vars[3],
framework=fw),
vars[0],
vars[1],
framework=fw)
expected_logp = dist_cls(expected_logits, policy.model).logp(
train_batch_[SampleBatch.ACTIONS])
adv = train_batch_[Postprocessing.ADVANTAGES]
if sess:
expected_logp = sess.run(expected_logp)
elif fw == "torch":
expected_logp = expected_logp.detach().cpu().numpy()
adv = adv.detach().cpu().numpy()
else:
expected_logp = expected_logp.numpy()
expected_loss = -np.mean(expected_logp * adv)
check(results, expected_loss, decimals=4)