def evaluate(
self,
evaluator: Evaluator,
logged_actions: Optional[np.ndarray],
logged_propensities: Optional[np.ndarray],
logged_rewards: Optional[np.ndarray],
logged_values: Optional[np.ndarray],
):
self.model_propensities, model_values_on_logged_actions, maxq_action_idxs = (
None,
None,
None,
)
if self.all_action_scores is not None:
self.all_action_scores = self.all_action_scores.cpu().numpy()
self.model_propensities = Evaluator.softmax(
self.all_action_scores, self.rl_temperature)
maxq_action_idxs = self.all_action_scores.argmax(axis=1)
if logged_actions is not None:
model_values_on_logged_actions = np.sum(
(logged_actions * self.all_action_scores),
axis=1,
keepdims=True)
evaluator.report(
self.loss.cpu().numpy(),
logged_actions,
logged_propensities,
logged_rewards,
logged_values,
self.model_propensities,
self.all_action_scores,
model_values_on_logged_actions,
maxq_action_idxs,
)
def policy(self, states):
with core.DeviceScope(self.c2_device):
if isinstance(self.trainer, DiscreteActionTrainer):
workspace.FeedBlob("states", states)
elif isinstance(self.trainer, ContinuousActionDQNTrainer):
num_actions = len(self.trainer.action_normalization_parameters)
actions = np.eye(num_actions, dtype=np.float32)
actions = np.tile(actions, reps=(len(states), 1))
states = np.repeat(states, repeats=num_actions, axis=0)
workspace.FeedBlob("states", states)
workspace.FeedBlob("actions", actions)
else:
raise NotImplementedError(
"Invalid trainer passed to GymPredictor")
workspace.RunNetOnce(self.trainer.internal_policy_model.net)
policy_output_blob = self.trainer.internal_policy_output
q_scores = workspace.FetchBlob(policy_output_blob)
if isinstance(self.trainer, DiscreteActionTrainer):
assert q_scores.shape[0] == 1
q_scores = q_scores[0]
q_scores_softmax = Evaluator.softmax(q_scores.reshape(
1, -1), self.trainer.rl_temperature)[0]
if np.isnan(
q_scores_softmax).any() or np.max(q_scores_softmax) < 1e-3:
q_scores_softmax[:] = 1.0 / q_scores_softmax.shape[0]
policies = [
np.argmax(q_scores),
np.random.choice(q_scores.shape[0], p=q_scores_softmax),
]
return policies
def evaluate(
self,
evaluator: Evaluator,
logged_actions: Optional[np.ndarray],
logged_propensities: Optional[np.ndarray],
logged_values: Optional[np.ndarray],
):
workspace.RunNet(self.all_q_score_model.net)
all_action_scores = workspace.FetchBlob(self.all_q_score_output)
maxq_action_idxs = workspace.FetchBlob(self.maxq_action_idxs)
model_values_on_logged_actions = np.sum(
(logged_actions * all_action_scores), axis=1, keepdims=True)
model_propensities = Evaluator.softmax(all_action_scores,
self.rl_temperature)
logged_rewards = workspace.FetchBlob("rewards")
evaluator.report(
workspace.FetchBlob(self.loss_blob),
logged_actions,
logged_propensities,
logged_rewards,
logged_values,
model_propensities,
all_action_scores,
model_values_on_logged_actions,
maxq_action_idxs,
)
def evaluate(
self,
evaluator: Evaluator,
logged_actions: torch.Tensor,
logged_propensities: Optional[torch.Tensor],
logged_rewards: torch.Tensor,
logged_values: Optional[torch.Tensor],
):
self.model_propensities, model_values_on_logged_actions, maxq_action_idxs = (
None,
None,
None,
)
if self.all_action_scores is not None:
self.all_action_scores = self.all_action_scores
self.model_propensities = Evaluator.softmax(
self.all_action_scores.cpu().numpy(), self.rl_temperature
)
maxq_action_idxs = self.all_action_scores.argmax(dim=1, keepdim=True)
if logged_actions is not None:
model_values_on_logged_actions = (
torch.sum(
(logged_actions * self.all_action_scores), dim=1, keepdim=True
)
.cpu()
.numpy()
)
evaluator.report(
self.loss.cpu().numpy(),
logged_actions.cpu().numpy(),
logged_propensities.cpu().numpy()
if logged_propensities is not None
else None,
logged_rewards.cpu().numpy(),
logged_values.cpu().numpy() if logged_values is not None else None,
self.model_propensities,
self.reward_estimates.cpu().numpy(),
self.all_action_scores.cpu().numpy(),
model_values_on_logged_actions,
maxq_action_idxs,
)
def policy(self, states):
with core.DeviceScope(self.c2_device):
if isinstance(self.trainer, DiscreteActionTrainer):
workspace.FeedBlob("states", states)
else:
raise NotImplementedError(
"Invalid trainer passed to GymPredictor")
workspace.RunNetOnce(self.trainer.internal_policy_model.net)
policy_output_blob = self.trainer.internal_policy_output
q_scores = workspace.FetchBlob(policy_output_blob)
if isinstance(self.trainer, DiscreteActionTrainer):
assert q_scores.shape[0] == 1
q_scores = q_scores[0]
q_scores_softmax = Evaluator.softmax(q_scores.reshape(
1, -1), self.trainer.rl_temperature)[0]
if np.isnan(
q_scores_softmax).any() or np.max(q_scores_softmax) < 1e-3:
q_scores_softmax[:] = 1.0 / q_scores_softmax.shape[0]
policies = [
np.argmax(q_scores),
np.random.choice(q_scores.shape[0], p=q_scores_softmax),
]
return policies
def policy(self, states):
if isinstance(self.trainer, DQNTrainer):
input = states
elif isinstance(self.trainer, ParametricDQNTrainer):
num_actions = len(self.trainer.action_normalization_parameters)
actions = np.eye(num_actions, dtype=np.float32)
actions = np.tile(actions, reps=(len(states), 1))
states = np.repeat(states, repeats=num_actions, axis=0)
input = np.hstack((states, actions))
else:
raise NotImplementedError("Invalid trainer passed to GymPredictor")
q_scores = self.trainer.internal_prediction(input)
if isinstance(self.trainer, DQNTrainer):
assert q_scores.shape[0] == 1
q_scores = q_scores[0]
q_scores_softmax = Evaluator.softmax(q_scores.reshape(1, -1),
self.trainer.rl_temperature)[0]
if np.isnan(q_scores_softmax).any() or np.max(q_scores_softmax) < 1e-3:
q_scores_softmax[:] = 1.0 / q_scores_softmax.shape[0]
policies = [
np.argmax(q_scores),
np.random.choice(q_scores.shape[0], p=q_scores_softmax),
]
return policies
def evaluate(self, predictor):
# test only float features
predictions = predictor.predict(self.logged_states)
estimated_reward_values = predictor.estimate_reward(self.logged_states)
if isinstance(predictor.trainer, ParametricDQNTrainer):
predictions = predictions.reshape([-1, self._env.action_dim])
estimated_reward_values = estimated_reward_values.reshape(
[-1, self._env.action_dim])
value_error_sum = 0.0
reward_error_sum = 0.0
for i in range(len(self.logged_states)):
logged_action = self.logged_actions[i]
logged_value = self.logged_values[i][0]
target_value = predictions[i][logged_action]
value_error_sum += abs(logged_value - target_value)
logged_reward = self.logged_rewards[i][0]
estimated_reward = estimated_reward_values[i][logged_action]
reward_error_sum += abs(logged_reward - estimated_reward)
value_error_mean = value_error_sum / np.sum(np.abs(self.logged_values))
reward_error_mean = reward_error_sum / np.sum(
np.abs(self.logged_rewards))
logger.info("EVAL Q-Value MAE ERROR: {0:.3f}".format(value_error_mean))
self.mc_loss.append(value_error_mean)
logger.info("EVAL REWARD MAE ERROR: {0:.3f}".format(reward_error_mean))
self.reward_loss.append(reward_error_mean)
target_propensities = Evaluator.softmax(
predictions, GymEvaluator.SOFTMAX_TEMPERATURE)
reward_inverse_propensity_score, reward_direct_method, reward_doubly_robust = self.doubly_robust_one_step_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_propensities,
target_propensities,
estimated_reward_values,
)
self.reward_inverse_propensity_score.append(
reward_inverse_propensity_score)
self.reward_direct_method.append(reward_direct_method)
self.reward_doubly_robust.append(reward_doubly_robust)
logger.info(
"Reward Inverse Propensity Score : normalized {0:.3f} raw {1:.3f}"
.format(
reward_inverse_propensity_score.normalized,
reward_inverse_propensity_score.raw,
))
logger.info(
"Reward Direct Method : normalized {0:.3f} raw {1:.3f}"
.format(reward_direct_method.normalized, reward_direct_method.raw))
logger.info(
"Reward Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}"
.format(reward_doubly_robust.normalized, reward_doubly_robust.raw))
value_inverse_propensity_score, value_direct_method, value_doubly_robust = self.doubly_robust_one_step_policy_estimation(
self.logged_actions_one_hot,
self.logged_values,
self.logged_propensities,
target_propensities,
predictions,
)
self.value_inverse_propensity_score.append(
value_inverse_propensity_score)
self.value_direct_method.append(value_direct_method)
self.value_doubly_robust.append(value_doubly_robust)
logger.info(
"Value Inverse Propensity Score : normalized {0:.3f} raw {1:.3f}"
.format(
value_inverse_propensity_score.normalized,
value_inverse_propensity_score.raw,
))
logger.info(
"Value Direct Method : normalized {0:.3f} raw {1:.3f}"
.format(value_direct_method.normalized, value_direct_method.raw))
logger.info(
"Value One-Step Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}"
.format(value_doubly_robust.normalized, value_doubly_robust.raw))
sequential_doubly_robust = self.doubly_robust_sequential_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_terminals,
self.logged_propensities,
target_propensities,
predictions,
)
self.value_sequential_doubly_robust.append(sequential_doubly_robust)
logger.info(
"Value Sequential Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}"
.format(sequential_doubly_robust.normalized,
sequential_doubly_robust.raw))
weighted_doubly_robust = self.weighted_doubly_robust_sequential_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_terminals,
self.logged_propensities,
target_propensities,
predictions,
num_j_steps=1,
whether_self_normalize_importance_weights=True,
)
self.value_weighted_doubly_robust.append(weighted_doubly_robust)
logger.info(
"Value Weighted Sequential Doubly Robust P.E. : noramlized {0:.3f} raw {1:.3f}"
.format(weighted_doubly_robust.normalized,
weighted_doubly_robust.raw))
magic_doubly_robust = self.weighted_doubly_robust_sequential_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_terminals,
self.logged_propensities,
target_propensities,
predictions,
num_j_steps=GymEvaluator.NUM_J_STEPS_FOR_MAGIC_ESTIMATOR,
whether_self_normalize_importance_weights=True,
)
self.value_magic_doubly_robust.append(magic_doubly_robust)
logger.info(
"Value Magic Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}"
.format(magic_doubly_robust.normalized, magic_doubly_robust.raw))
avg_rewards, avg_discounted_rewards = self._env.run_ep_n_times(
100, predictor, test=True)
episode_starts = np.nonzero(self.logged_terminals.squeeze())[0] + 1
logged_discounted_performance = (self.logged_values[0][0] + np.sum(
self.logged_values[episode_starts[:-1]])) / np.sum(
self.logged_terminals)
true_discounted_value_PE = (avg_discounted_rewards /
logged_discounted_performance)
self.true_discounted_value_PE.append(true_discounted_value_PE)
logger.info(
"True Discounted Value P.E : normalized {0:.3f} raw {1:.3f}"
.format(true_discounted_value_PE, avg_discounted_rewards))
logged_performance = np.sum(self.logged_rewards) / np.sum(
self.logged_terminals)
true_value_PE = avg_rewards / logged_performance
self.true_value_PE.append(true_value_PE)
logger.info(
"True Value P.E : normalized {0:.3f} raw {1:.3f}"
.format(true_value_PE, avg_rewards))
def evaluate(self, predictor):
# Test feeding float features & int features
if self.use_int_features:
float_features, int_features = self._split_int_and_float_features(
self.logged_states)
# Since all gridworld features are float types, swap these so
# all inputs are now int_features for testing purpose
float_features, int_features = int_features, float_features
prediction_string = predictor.predict(float_features, int_features)
# Test only feeding float features
else:
prediction_string = predictor.predict(self.logged_states)
# Convert action string to integer
prediction = np.zeros([len(prediction_string),
len(self._env.ACTIONS)],
dtype=np.float32)
for x in range(len(self.logged_states)):
for action_index, action in enumerate(self._env.ACTIONS):
prediction[x][action_index] = prediction_string[x].get(
action, 1e-9)
# Print out scores using all states
all_states = []
for x in self._env.STATES:
all_states.append({x: 1.0})
if self.use_int_features:
all_states_float, all_states_int = self._split_int_and_float_features(
all_states)
all_states_prediction_string = predictor.predict(
all_states_float, all_states_int)
else:
all_states_prediction_string = predictor.predict(all_states)
all_states_prediction = np.zeros(
[len(all_states_prediction_string),
len(self._env.ACTIONS)],
dtype=np.float32,
)
for x in range(len(all_states)):
for action_index, action in enumerate(self._env.ACTIONS):
all_states_prediction[x][
action_index] = all_states_prediction_string[x].get(
action, 1e-9)
print(all_states_prediction[:, 0].reshape(5, 5), "\n")
print(all_states_prediction[:, 1].reshape(5, 5), "\n")
print(all_states_prediction[:, 2].reshape(5, 5), "\n")
print(all_states_prediction[:, 3].reshape(5, 5), "\n")
error_sum = 0.0
num_error_prints = 0
for x in range(len(self.logged_states)):
logged_value = self.logged_values[x][0]
target_value = prediction_string[x].get(self.logged_actions[x],
1e-9)
error = abs(logged_value - target_value)
if num_error_prints < 10 and error > 0.2:
print(
"GOT THIS STATE WRONG: ",
x,
self._env._pos(list(self.logged_states[x].keys())[0]),
self.logged_actions[x],
logged_value,
target_value,
)
num_error_prints += 1
if num_error_prints == 10:
print("MAX ERRORS PRINTED")
error_sum += error
error_mean = error_sum / float(len(self.logged_states))
logger.info("EVAL ERROR: {0:.3f}".format(error_mean))
self.mc_loss.append(error_mean)
target_propensities = Evaluator.softmax(
prediction, GridworldEvaluator.SOFTMAX_TEMPERATURE)
reward_inverse_propensity_score, reward_direct_method, reward_doubly_robust = self.doubly_robust_one_step_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_propensities,
target_propensities,
self.estimated_reward_values,
)
self.reward_inverse_propensity_score.append(
reward_inverse_propensity_score)
self.reward_direct_method.append(reward_direct_method)
self.reward_doubly_robust.append(reward_doubly_robust)
logger.info(
"Reward Inverse Propensity Score : normalized {0:.3f} raw {1:.3f}"
.format(
reward_inverse_propensity_score.normalized,
reward_inverse_propensity_score.raw,
))
logger.info(
"Reward Direct Method : normalized {0:.3f} raw {1:.3f}"
.format(reward_direct_method.normalized, reward_direct_method.raw))
logger.info(
"Reward Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}"
.format(reward_doubly_robust.normalized, reward_doubly_robust.raw))
value_inverse_propensity_score, value_direct_method, value_doubly_robust = self.doubly_robust_one_step_policy_estimation(
self.logged_actions_one_hot,
self.logged_values,
self.logged_propensities,
target_propensities,
self.estimated_ltv_values,
)
self.value_inverse_propensity_score.append(
value_inverse_propensity_score)
self.value_direct_method.append(value_direct_method)
self.value_doubly_robust.append(value_doubly_robust)
logger.info(
"Value Inverse Propensity Score : normalized {0:.3f} raw {1:.3f}"
.format(
value_inverse_propensity_score.normalized,
value_inverse_propensity_score.raw,
))
logger.info(
"Value Direct Method : normalized {0:.3f} raw {1:.3f}"
.format(value_direct_method.normalized, value_direct_method.raw))
logger.info(
"Value One-Step Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}"
.format(value_doubly_robust.normalized, value_doubly_robust.raw))
sequential_doubly_robust = self.doubly_robust_sequential_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_terminals,
self.logged_propensities,
target_propensities,
self.estimated_ltv_values,
)
self.value_sequential_doubly_robust.append(sequential_doubly_robust)
logger.info(
"Value Sequential Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}"
.format(sequential_doubly_robust.normalized,
sequential_doubly_robust.raw))
weighted_doubly_robust = self.weighted_doubly_robust_sequential_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_terminals,
self.logged_propensities,
target_propensities,
self.estimated_ltv_values,
num_j_steps=1,
whether_self_normalize_importance_weights=True,
)
self.value_weighted_doubly_robust.append(weighted_doubly_robust)
logger.info(
"Value Weighted Sequential Doubly Robust P.E. : noramlized {0:.3f} raw {1:.3f}"
.format(weighted_doubly_robust.normalized,
weighted_doubly_robust.raw))
magic_doubly_robust = self.weighted_doubly_robust_sequential_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_terminals,
self.logged_propensities,
target_propensities,
self.estimated_ltv_values,
num_j_steps=GridworldEvaluator.NUM_J_STEPS_FOR_MAGIC_ESTIMATOR,
whether_self_normalize_importance_weights=True,
)
self.value_magic_doubly_robust.append(magic_doubly_robust)
logger.info(
"Value Magic Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}"
.format(magic_doubly_robust.normalized, magic_doubly_robust.raw))
def evaluate_predictions(self, prediction, all_states_prediction):
print(all_states_prediction[:, 0].reshape(5, 5), "\n")
print(all_states_prediction[:, 1].reshape(5, 5), "\n")
print(all_states_prediction[:, 2].reshape(5, 5), "\n")
print(all_states_prediction[:, 3].reshape(5, 5), "\n")
error_sum = 0.0
num_error_prints = 0
for x in range(len(self.logged_states)):
int_action = self._env.action_to_index(self.logged_actions[x])
logged_value = self.logged_values[x][0]
target_value = prediction[x][int_action]
error = abs(logged_value - target_value)
if num_error_prints < 10 and error > 0.2:
print(
"GOT THIS STATE WRONG: ",
x,
self._env._pos(list(self.logged_states[x].keys())[0]),
self.logged_actions[x],
logged_value,
target_value,
)
num_error_prints += 1
if num_error_prints == 10:
print("MAX ERRORS PRINTED")
error_sum += error
error_mean = error_sum / float(len(self.logged_states))
logger.info("EVAL ERROR: {0:.3f}".format(error_mean))
self.mc_loss.append(error_mean)
target_propensities = Evaluator.softmax(
prediction, GridworldEvaluator.SOFTMAX_TEMPERATURE
)
reward_inverse_propensity_score, reward_direct_method, reward_doubly_robust = self.doubly_robust_one_step_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_propensities,
target_propensities,
self.estimated_reward_values,
)
self.reward_inverse_propensity_score.append(reward_inverse_propensity_score)
self.reward_direct_method.append(reward_direct_method)
self.reward_doubly_robust.append(reward_doubly_robust)
logger.info(
"Reward Inverse Propensity Score : normalized {0:.3f} raw {1:.3f}".format(
reward_inverse_propensity_score.normalized,
reward_inverse_propensity_score.raw,
)
)
logger.info(
"Reward Direct Method : normalized {0:.3f} raw {1:.3f}".format(
reward_direct_method.normalized, reward_direct_method.raw
)
)
logger.info(
"Reward Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}".format(
reward_doubly_robust.normalized, reward_doubly_robust.raw
)
)
value_inverse_propensity_score, value_direct_method, value_doubly_robust = self.doubly_robust_one_step_policy_estimation(
self.logged_actions_one_hot,
self.logged_values,
self.logged_propensities,
target_propensities,
self.estimated_ltv_values,
)
self.value_inverse_propensity_score.append(value_inverse_propensity_score)
self.value_direct_method.append(value_direct_method)
self.value_doubly_robust.append(value_doubly_robust)
logger.info(
"Value Inverse Propensity Score : normalized {0:.3f} raw {1:.3f}".format(
value_inverse_propensity_score.normalized,
value_inverse_propensity_score.raw,
)
)
logger.info(
"Value Direct Method : normalized {0:.3f} raw {1:.3f}".format(
value_direct_method.normalized, value_direct_method.raw
)
)
logger.info(
"Value One-Step Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}".format(
value_doubly_robust.normalized, value_doubly_robust.raw
)
)
sequential_doubly_robust = self.doubly_robust_sequential_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_terminals,
self.logged_propensities,
target_propensities,
self.estimated_ltv_values,
)
self.value_sequential_doubly_robust.append(sequential_doubly_robust)
logger.info(
"Value Sequential Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}".format(
sequential_doubly_robust.normalized, sequential_doubly_robust.raw
)
)
weighted_doubly_robust = self.weighted_doubly_robust_sequential_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_terminals,
self.logged_propensities,
target_propensities,
self.estimated_ltv_values,
num_j_steps=1,
whether_self_normalize_importance_weights=True,
)
self.value_weighted_doubly_robust.append(weighted_doubly_robust)
logger.info(
"Value Weighted Sequential Doubly Robust P.E. : noramlized {0:.3f} raw {1:.3f}".format(
weighted_doubly_robust.normalized, weighted_doubly_robust.raw
)
)
magic_doubly_robust = self.weighted_doubly_robust_sequential_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_terminals,
self.logged_propensities,
target_propensities,
self.estimated_ltv_values,
num_j_steps=GridworldEvaluator.NUM_J_STEPS_FOR_MAGIC_ESTIMATOR,
whether_self_normalize_importance_weights=True,
)
self.value_magic_doubly_robust.append(magic_doubly_robust)
logger.info(
"Value Magic Doubly Robust P.E. : normalized {0:.3f} raw {1:.3f}".format(
magic_doubly_robust.normalized, magic_doubly_robust.raw
)
)
def train(self,
training_samples: TrainingDataPage,
evaluator: Optional[Evaluator] = None):
if self.minibatch == 0:
# Assume that the tensors are the right shape after the first minibatch
assert (training_samples.states.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.states.shape)
assert training_samples.actions.shape == torch.Size([
self.minibatch_size, len(self._actions)
]), "Invalid shape: " + str(training_samples.actions.shape)
assert training_samples.rewards.shape == torch.Size(
[self.minibatch_size,
1]), "Invalid shape: " + str(training_samples.rewards.shape)
assert (training_samples.next_states.shape ==
training_samples.states.shape), "Invalid shape: " + str(
training_samples.next_states.shape)
assert (training_samples.not_terminals.shape ==
training_samples.rewards.shape), "Invalid shape: " + str(
training_samples.not_terminals.shape)
if training_samples.possible_next_actions is not None:
assert (
training_samples.possible_next_actions.shape ==
training_samples.actions.shape), "Invalid shape: " + str(
training_samples.possible_next_actions.shape)
if training_samples.propensities is not None:
assert (training_samples.propensities.shape == training_samples
.rewards.shape), "Invalid shape: " + str(
training_samples.propensities.shape)
# Apply reward boost if specified
reward_boosts = torch.sum(training_samples.actions.float() *
self.reward_boosts,
dim=1,
keepdim=True)
boosted_rewards = training_samples.rewards + reward_boosts
self.minibatch += 1
states = training_samples.states.detach().requires_grad_(True)
actions = training_samples.actions
rewards = boosted_rewards
next_states = training_samples.next_states
discount_tensor = torch.full(training_samples.time_diffs.shape,
self.gamma).type(self.dtype)
not_done_mask = training_samples.not_terminals
if self.use_seq_num_diff_as_time_diff:
discount_tensor = discount_tensor.pow(training_samples.time_diffs)
if self.maxq_learning:
# Compute max a' Q(s', a') over all possible actions using target network
possible_next_actions = training_samples.possible_next_actions
next_q_values = self.get_max_q_values(next_states,
possible_next_actions,
self.double_q_learning)
else:
# SARSA
next_actions = training_samples.next_actions
next_q_values = self.get_next_action_q_values(
next_states, next_actions)
filtered_next_q_vals = next_q_values * not_done_mask
if self.minibatch < self.reward_burnin:
target_q_values = rewards
else:
target_q_values = rewards + (discount_tensor *
filtered_next_q_vals)
# Get Q-value of action taken
all_q_values = self.q_network(states)
self.all_action_scores = all_q_values.detach()
q_values = torch.sum(all_q_values * actions, 1, keepdim=True)
loss = self.q_network_loss(q_values, target_q_values)
self.loss = loss.detach()
self.q_network_optimizer.zero_grad()
loss.backward()
if self.gradient_handler:
self.gradient_handler(self.q_network.parameters())
self.q_network_optimizer.step()
if self.minibatch < self.reward_burnin:
# Reward burnin: force target network
self._soft_update(self.q_network, self.q_network_target, 1.0)
else:
# Use the soft update rule to update target network
self._soft_update(self.q_network, self.q_network_target, self.tau)
# get reward estimates
reward_estimates = self.reward_network(states)
self.reward_estimates = reward_estimates.detach()
reward_estimates_for_logged_actions = reward_estimates.gather(
1, actions.argmax(dim=1, keepdim=True))
reward_loss = F.mse_loss(reward_estimates_for_logged_actions, rewards)
self.reward_network_optimizer.zero_grad()
reward_loss.backward()
self.reward_network_optimizer.step()
self.loss_reporter.report(td_loss=float(self.loss),
reward_loss=float(reward_loss))
training_metadata = {}
if evaluator is not None:
model_propensities = torch.from_numpy(
Evaluator.softmax(self.all_action_scores.cpu().numpy(),
self.rl_temperature))
cpe_stats = BatchStatsForCPE(
logged_actions=training_samples.actions,
logged_propensities=training_samples.propensities,
logged_rewards=rewards,
logged_values=None, # Compute at end of each epoch for CPE
model_propensities=model_propensities,
model_rewards=self.reward_estimates,
model_values=self.all_action_scores,
model_values_on_logged_actions=
None, # Compute at end of each epoch for CPE
model_action_idxs=self.all_action_scores.argmax(dim=1,
keepdim=True),
)
evaluator.report(cpe_stats)
training_metadata["model_rewards"] = self.reward_estimates.cpu(
).numpy()
return training_metadata
def train(self, training_samples: TrainingDataPage):
if self.minibatch == 0:
# Assume that the tensors are the right shape after the first minibatch
assert (training_samples.states.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.states.shape)
assert training_samples.actions.shape == torch.Size([
self.minibatch_size, len(self._actions)
]), "Invalid shape: " + str(training_samples.actions.shape)
assert training_samples.rewards.shape == torch.Size(
[self.minibatch_size,
1]), "Invalid shape: " + str(training_samples.rewards.shape)
assert (training_samples.next_states.shape ==
training_samples.states.shape), "Invalid shape: " + str(
training_samples.next_states.shape)
assert (training_samples.not_terminal.shape ==
training_samples.rewards.shape), "Invalid shape: " + str(
training_samples.not_terminal.shape)
if training_samples.possible_next_actions_mask is not None:
assert (
training_samples.possible_next_actions_mask.shape ==
training_samples.actions.shape), (
"Invalid shape: " +
str(training_samples.possible_next_actions_mask.shape))
if training_samples.propensities is not None:
assert (training_samples.propensities.shape == training_samples
.rewards.shape), "Invalid shape: " + str(
training_samples.propensities.shape)
if training_samples.metrics is not None:
assert (
training_samples.metrics.shape[0] == self.minibatch_size
), "Invalid shape: " + str(training_samples.metrics.shape)
boosted_rewards = self.boost_rewards(training_samples.rewards,
training_samples.actions)
self.minibatch += 1
states = training_samples.states.detach().requires_grad_(True)
actions = training_samples.actions
rewards = boosted_rewards
discount_tensor = torch.full(training_samples.time_diffs.shape,
self.gamma).type(self.dtype)
not_done_mask = training_samples.not_terminal
if self.use_seq_num_diff_as_time_diff:
discount_tensor = discount_tensor.pow(training_samples.time_diffs)
all_next_q_values, all_next_q_values_target = self.get_detached_q_values(
training_samples.next_states)
if self.maxq_learning:
# Compute max a' Q(s', a') over all possible actions using target network
next_q_values, max_q_action_idxs = self.get_max_q_values(
all_next_q_values,
all_next_q_values_target,
training_samples.possible_next_actions_mask,
)
else:
# SARSA
next_q_values, max_q_action_idxs = self.get_max_q_values(
all_next_q_values,
all_next_q_values_target,
training_samples.next_actions,
)
filtered_next_q_vals = next_q_values * not_done_mask
if self.minibatch < self.reward_burnin:
target_q_values = rewards
else:
target_q_values = rewards + (discount_tensor *
filtered_next_q_vals)
# Get Q-value of action taken
all_q_values = self.q_network(states)
self.all_action_scores = all_q_values.detach()
q_values = torch.sum(all_q_values * actions, 1, keepdim=True)
loss = self.q_network_loss(q_values, target_q_values)
self.loss = loss.detach()
self.q_network_optimizer.zero_grad()
loss.backward()
if self.gradient_handler:
self.gradient_handler(self.q_network.parameters())
self.q_network_optimizer.step()
if self.minibatch < self.reward_burnin:
# Reward burnin: force target network
self._soft_update(self.q_network, self.q_network_target, 1.0)
else:
# Use the soft update rule to update target network
self._soft_update(self.q_network, self.q_network_target, self.tau)
if training_samples.metrics is None:
metrics_reward_concat_real_vals = training_samples.rewards
else:
metrics_reward_concat_real_vals = torch.cat(
(training_samples.metrics, training_samples.rewards), dim=1)
######### Train separate reward network for CPE evaluation #############
reward_estimates = self.reward_network(states)
logged_action_idxs = actions.argmax(dim=1, keepdim=True)
reward_estimates_for_logged_actions = reward_estimates.gather(
1, self.reward_idx_offsets + logged_action_idxs)
reward_loss = F.mse_loss(reward_estimates_for_logged_actions,
metrics_reward_concat_real_vals)
self.reward_network_optimizer.zero_grad()
reward_loss.backward()
self.reward_network_optimizer.step()
######### Train separate q-network for CPE evaluation #############
metric_q_values = self.q_network_cpe(states).gather(
1, self.reward_idx_offsets + logged_action_idxs)
metric_target_q_values = self.q_network_cpe_target(states).detach()
max_q_values_metrics = metric_target_q_values.gather(
1, self.reward_idx_offsets + max_q_action_idxs)
filtered_max_q_values_metrics = max_q_values_metrics * not_done_mask
if self.minibatch < self.reward_burnin:
target_metric_q_values = metrics_reward_concat_real_vals
else:
target_metric_q_values = metrics_reward_concat_real_vals + (
discount_tensor * filtered_max_q_values_metrics)
metric_q_value_loss = self.q_network_loss(metric_q_values,
target_metric_q_values)
self.q_network_cpe.zero_grad()
metric_q_value_loss.backward()
self.q_network_cpe_optimizer.step()
if self.minibatch < self.reward_burnin:
# Reward burnin: force target network
self._soft_update(self.q_network_cpe, self.q_network_cpe_target,
1.0)
else:
# Use the soft update rule to update target network
self._soft_update(self.q_network_cpe, self.q_network_cpe_target,
self.tau)
model_propensities = torch.from_numpy(
Evaluator.softmax(self.all_action_scores.cpu().numpy(),
self.rl_temperature))
self.loss_reporter.report(
td_loss=self.loss,
reward_loss=reward_loss,
logged_actions=logged_action_idxs,
logged_propensities=training_samples.propensities,
logged_rewards=rewards,
logged_values=None, # Compute at end of each epoch for CPE
model_propensities=model_propensities,
model_rewards=reward_estimates[:,
torch.arange(
self.reward_idx_offsets[0],
self.reward_idx_offsets[0] +
self.num_actions,
), ],
model_values=self.all_action_scores,
model_values_on_logged_actions=
None, # Compute at end of each epoch for CPE
model_action_idxs=self.all_action_scores.argmax(dim=1,
keepdim=True),
)
training_metadata = {}
training_metadata["model_rewards"] = reward_estimates.detach().cpu(
).numpy()
return training_metadata
def evaluate(self, predictor):
# Test feeding float features & int features
if self.use_int_features:
float_features, int_features = self._split_int_and_float_features(
self.logged_states)
# Since all gridworld features are float types, swap these so
# all inputs are now int_features for testing purpose
float_features, int_features = int_features, float_features
prediction_string = predictor.predict(float_features, int_features)
# Test only feeding float features
else:
prediction_string = predictor.predict(self.logged_states)
# Convert action string to integer
prediction = np.zeros([len(prediction_string),
len(self._env.ACTIONS)],
dtype=np.float32)
for x in range(len(self.logged_states)):
for action_index, action in enumerate(self._env.ACTIONS):
prediction[x][action_index] = prediction_string[x][action]
error_sum = 0.0
for x in range(len(self.logged_states)):
logged_value = self.logged_values[x][0]
target_value = prediction_string[x][self.logged_actions[x]]
error_sum += abs(logged_value - target_value)
error_mean = error_sum / float(len(self.logged_states))
logger.info("EVAL ERROR: {0:.3f}".format(error_mean))
self.mc_loss.append(error_mean)
target_propensities = Evaluator.softmax(
prediction, GridworldEvaluator.SOFTMAX_TEMPERATURE)
value_inverse_propensity_score, value_direct_method, value_doubly_robust = self.doubly_robust_policy_estimation(
self.logged_actions_one_hot,
self.logged_values,
self.logged_propensities,
target_propensities,
self.estimated_ltv_values,
)
self.value_inverse_propensity_score.append(
value_inverse_propensity_score)
self.value_direct_method.append(value_direct_method)
self.value_doubly_robust.append(value_doubly_robust)
logger.info("Value Inverse Propensity Score : {0:.3f}".format(
value_inverse_propensity_score))
logger.info("Value Direct Method : {0:.3f}".format(
value_direct_method))
logger.info("Value Doubly Robust P.E. : {0:.3f}".format(
value_doubly_robust))
reward_inverse_propensity_score, reward_direct_method, reward_doubly_robust = self.doubly_robust_policy_estimation(
self.logged_actions_one_hot,
self.logged_rewards,
self.logged_propensities,
target_propensities,
self.estimated_reward_values,
)
self.reward_inverse_propensity_score.append(
reward_inverse_propensity_score)
self.reward_direct_method.append(reward_direct_method)
self.reward_doubly_robust.append(reward_doubly_robust)
logger.info("Reward Inverse Propensity Score: {0:.3f}".format(
reward_inverse_propensity_score))
logger.info("Reward Direct Method : {0:.3f}".format(
reward_direct_method))
logger.info("Reward Doubly Robust P.E. : {0:.3f}".format(
reward_doubly_robust))
