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[RLlib] CQL: Bug fixes and OPE example added to test and offline_rl.py example. (#15761)

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Sven Mika 2021-05-13 09:17:23 +02:00 • committed by GitHub
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8 changed files with 149 additions and 90 deletions
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2
rllib/BUILD
View file

@ -2224,7 +2224,7 @@ py_test(
name = "examples/parametric_actions_cartpole_pg_torch", name = "examples/parametric_actions_cartpole_pg_torch",
main = "examples/parametric_actions_cartpole.py", main = "examples/parametric_actions_cartpole.py",
tags = ["examples", "examples_P"], tags = ["examples", "examples_P"],
size = "small", size = "medium",
srcs = ["examples/parametric_actions_cartpole.py"], srcs = ["examples/parametric_actions_cartpole.py"],
args = ["--as-test", "--torch", "--stop-reward=60.0", "--run=PG"] args = ["--as-test", "--torch", "--stop-reward=60.0", "--run=PG"]
) )

7
rllib/agents/cql/cql_torch_policy.py
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@ -14,6 +14,7 @@ from ray.rllib.agents.sac.sac_torch_policy import _get_dist_class, stats, \
build_sac_model_and_action_dist, optimizer_fn, ComputeTDErrorMixin, \ build_sac_model_and_action_dist, optimizer_fn, ComputeTDErrorMixin, \
TargetNetworkMixin, setup_late_mixins, action_distribution_fn TargetNetworkMixin, setup_late_mixins, action_distribution_fn
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
from ray.rllib.policy.policy import LEARNER_STATS_KEY
from ray.rllib.policy.policy_template import build_policy_class from ray.rllib.policy.policy_template import build_policy_class
from ray.rllib.models.modelv2 import ModelV2 from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils.numpy import SMALL_NUMBER, MIN_LOG_NN_OUTPUT, \ from ray.rllib.utils.numpy import SMALL_NUMBER, MIN_LOG_NN_OUTPUT, \
@ -335,7 +336,11 @@ def compute_gradients_fn(policy, postprocessed_batch):
batches = [policy._lazy_tensor_dict(postprocessed_batch)] batches = [policy._lazy_tensor_dict(postprocessed_batch)]
model = policy.model model = policy.model
policy._loss(policy, model, policy.dist_class, batches[0]) policy._loss(policy, model, policy.dist_class, batches[0])
return [None, dict()] stats = {
LEARNER_STATS_KEY: policy._convert_to_non_torch_type(
cql_stats(policy, batches[0]))
}
return [None, stats]
def apply_gradients_fn(policy, gradients): def apply_gradients_fn(policy, gradients):

34
rllib/agents/cql/tests/test_cql.py
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@ -4,9 +4,12 @@ import unittest
import ray import ray
import ray.rllib.agents.cql as cql import ray.rllib.agents.cql as cql
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.test_utils import check_compute_single_action, \ from ray.rllib.utils.test_utils import check_compute_single_action, \
framework_iterator framework_iterator
torch, _ = try_import_torch()
class TestCQL(unittest.TestCase): class TestCQL(unittest.TestCase):
@classmethod @classmethod
@ -43,14 +46,43 @@ class TestCQL(unittest.TestCase):
config["rollout_fragment_length"] = 1 config["rollout_fragment_length"] = 1
config["train_batch_size"] = 10 config["train_batch_size"] = 10
# Switch on off-policy evaluation.
config["input_evaluation"] = ["is"]
num_iterations = 2 num_iterations = 2
# Test for tf framework (torch not implemented yet). # Test for tf framework (torch not implemented yet).
for _ in framework_iterator(config, frameworks=("torch")): for _ in framework_iterator(config, frameworks=("torch")):
trainer = cql.CQLTrainer(config=config) trainer = cql.CQLTrainer(config=config)
for i in range(num_iterations): for i in range(num_iterations):
trainer.train() print(trainer.train())
check_compute_single_action(trainer) check_compute_single_action(trainer)
# Get policy, model, and replay-buffer.
pol = trainer.get_policy()
cql_model = pol.model
from ray.rllib.agents.cql.cql import replay_buffer
# Example on how to do evaluation on the trained Trainer
# using the data from our buffer.
# Get a sample (MultiAgentBatch -> SampleBatch).
batch = replay_buffer.replay().policy_batches["default_policy"]
obs = torch.from_numpy(batch["obs"])
# Pass the observations through our model to get the
# features, which then to pass through the Q-head.
model_out, _ = cql_model({"obs": obs})
# The estimated Q-values from the (historic) actions in the batch.
q_values_old = cql_model.get_q_values(
model_out, torch.from_numpy(batch["actions"]))
# The estimated Q-values for the new actions computed
# by our trainer policy.
actions_new = pol.compute_actions_from_input_dict({"obs": obs})[0]
q_values_new = cql_model.get_q_values(
model_out, torch.from_numpy(actions_new))
print(f"Q-val batch={q_values_old}")
print(f"Q-val policy={q_values_new}")
trainer.stop() trainer.stop()

2
rllib/agents/marwil/marwil.py
View file

@ -13,7 +13,7 @@ from ray.rllib.execution.metric_ops import StandardMetricsReporting
DEFAULT_CONFIG = with_common_config({ DEFAULT_CONFIG = with_common_config({
# You should override this to point to an offline dataset (see agent.py). # You should override this to point to an offline dataset (see agent.py).
"input": "sampler", "input": "sampler",
# Use importance sampling estimators for reward # Use importance sampling estimators for reward.
"input_evaluation": ["is", "wis"], "input_evaluation": ["is", "wis"],
# If true, use the Generalized Advantage Estimator (GAE) # If true, use the Generalized Advantage Estimator (GAE)

2
rllib/agents/marwil/tests/test_marwil.py
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@ -57,7 +57,7 @@ class TestMARWIL(unittest.TestCase):
for i in range(num_iterations): for i in range(num_iterations):
eval_results = trainer.train().get("evaluation") eval_results = trainer.train().get("evaluation")
if eval_results: if eval_results:
print("iter={} R={}".format( print("iter={} R={} ".format(
i, eval_results["episode_reward_mean"])) i, eval_results["episode_reward_mean"]))
# Learn until some reward is reached on an actual live env. # Learn until some reward is reached on an actual live env.
if eval_results["episode_reward_mean"] > min_reward: if eval_results["episode_reward_mean"] > min_reward:

32
rllib/examples/offline_rl.py
View file

@ -80,7 +80,7 @@ if __name__ == "__main__":
# Check, whether we can learn from the given file in `num_iterations` # Check, whether we can learn from the given file in `num_iterations`
# iterations, up to a reward of `min_reward`. # iterations, up to a reward of `min_reward`.
num_iterations = 50 num_iterations = 5
min_reward = -300 min_reward = -300
# Test for torch framework (tf not implemented yet). # Test for torch framework (tf not implemented yet).
@ -99,17 +99,39 @@ if __name__ == "__main__":
raise ValueError("CQLTrainer did not reach {} reward from expert " raise ValueError("CQLTrainer did not reach {} reward from expert "
"offline data!".format(min_reward)) "offline data!".format(min_reward))
# Get policy, model, and replay-buffer.
pol = trainer.get_policy()
cql_model = pol.model
from ray.rllib.agents.cql.cql import replay_buffer
# If you would like to query CQL's learnt Q-function for arbitrary # If you would like to query CQL's learnt Q-function for arbitrary
# (cont.) actions, do the following: # (cont.) actions, do the following:
obs_batch = torch.from_numpy(np.random.random(size=(5, 3))) obs_batch = torch.from_numpy(np.random.random(size=(5, 3)))
action_batch = torch.from_numpy(np.random.random(size=(5, 1))) action_batch = torch.from_numpy(np.random.random(size=(5, 1)))
q_values = cql_model.get_q_values(obs_batch, action_batch)
cql_model = trainer.get_policy().model
q_values = cql_model.get_q_values([obs_batch], [action_batch])
# If you are using the "twin_q", there'll be 2 Q-networks and # If you are using the "twin_q", there'll be 2 Q-networks and
# we usually consider the min of the 2 outputs, like so: # we usually consider the min of the 2 outputs, like so:
twin_q_values = cql_model.get_twin_q_values([obs_batch], [action_batch]) twin_q_values = cql_model.get_twin_q_values(obs_batch, action_batch)
final_q_values = torch.min(q_values, twin_q_values) final_q_values = torch.min(q_values, twin_q_values)
print(final_q_values) print(final_q_values)
# Example on how to do evaluation on the trained Trainer
# using the data from our buffer.
# Get a sample (MultiAgentBatch -> SampleBatch).
batch = replay_buffer.replay().policy_batches["default_policy"]
obs = torch.from_numpy(batch["obs"])
# Pass the observations through our model to get the
# features, which then to pass through the Q-head.
model_out, _ = cql_model({"obs": obs})
# The estimated Q-values from the (historic) actions in the batch.
q_values_old = cql_model.get_q_values(model_out,
torch.from_numpy(batch["actions"]))
# The estimated Q-values for the new actions computed
# by our trainer policy.
actions_new = pol.compute_actions_from_input_dict({"obs": obs})[0]
q_values_new = cql_model.get_q_values(model_out,
torch.from_numpy(actions_new))
print(f"Q-val batch={q_values_old}")
print(f"Q-val policy={q_values_new}")
trainer.stop() trainer.stop()

80
rllib/offline/is_estimator.py
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@ -1,40 +1,40 @@
from ray.rllib.offline.off_policy_estimator import OffPolicyEstimator, \ from ray.rllib.offline.off_policy_estimator import OffPolicyEstimator, \
OffPolicyEstimate OffPolicyEstimate
from ray.rllib.utils.annotations import override from ray.rllib.utils.annotations import override
from ray.rllib.utils.typing import SampleBatchType from ray.rllib.utils.typing import SampleBatchType
class ImportanceSamplingEstimator(OffPolicyEstimator): class ImportanceSamplingEstimator(OffPolicyEstimator):
"""The step-wise IS estimator. """The step-wise IS estimator.
Step-wise IS estimator described in https://arxiv.org/pdf/1511.03722.pdf""" Step-wise IS estimator described in https://arxiv.org/pdf/1511.03722.pdf"""
@override(OffPolicyEstimator) @override(OffPolicyEstimator)
def estimate(self, batch: SampleBatchType) -> OffPolicyEstimate: def estimate(self, batch: SampleBatchType) -> OffPolicyEstimate:
self.check_can_estimate_for(batch) self.check_can_estimate_for(batch)
rewards, old_prob = batch["rewards"], batch["action_prob"] rewards, old_prob = batch["rewards"], batch["action_prob"]
new_prob = self.action_prob(batch) new_prob = self.action_prob(batch)
# calculate importance ratios # calculate importance ratios
p = [] p = []
for t in range(batch.count): for t in range(batch.count):
if t == 0: if t == 0:
pt_prev = 1.0 pt_prev = 1.0
else: else:
pt_prev = p[t - 1] pt_prev = p[t - 1]
p.append(pt_prev * new_prob[t] / old_prob[t]) p.append(pt_prev * new_prob[t] / old_prob[t])
# calculate stepwise IS estimate # calculate stepwise IS estimate
V_prev, V_step_IS = 0.0, 0.0 V_prev, V_step_IS = 0.0, 0.0
for t in range(batch.count): for t in range(batch.count):
V_prev += rewards[t] * self.gamma**t V_prev += rewards[t] * self.gamma**t
V_step_IS += p[t] * rewards[t] * self.gamma**t V_step_IS += p[t] * rewards[t] * self.gamma**t
estimation = OffPolicyEstimate( estimation = OffPolicyEstimate(
"is", { "is", {
"V_prev": V_prev, "V_prev": V_prev,
"V_step_IS": V_step_IS, "V_step_IS": V_step_IS,
"V_gain_est": V_step_IS / max(1e-8, V_prev), "V_gain_est": V_step_IS / max(1e-8, V_prev),
}) })
return estimation return estimation

80
rllib/tests/data/pendulum/small.json
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@ -1,40 +1,40 @@
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