RLlib Utilities

Here is a list of all the utilities available in RLlib.

Exploration API

Exploration is crucial in RL for enabling a learning agent to find new, potentially high-reward states by reaching unexplored areas of the environment.

RLlib has several built-in exploration components that the different algorithms use. You can also customize an algorithm’s exploration behavior by sub-classing the Exploration base class and implementing your own logic:

Built-in Exploration components

Exploration(action_space, *, framework, ...)	Implements an exploration strategy for Policies.
Random(action_space, *, model, framework, ...)	A random action selector (deterministic/greedy for explore=False).
StochasticSampling(action_space, *, ...[, ...])	An exploration that simply samples from a distribution.
EpsilonGreedy(action_space, *, framework[, ...])	Epsilon-greedy Exploration class that produces exploration actions.
GaussianNoise(action_space, *, framework, model)	An exploration that adds white noise to continuous actions.
OrnsteinUhlenbeckNoise(action_space, *, ...)	An exploration that adds Ornstein-Uhlenbeck noise to continuous actions.
RE3(action_space, *, framework, model[, ...])	Random Encoder for Efficient Exploration.
Curiosity(action_space, *, framework, model)	Implementation of: [1] Curiosity-driven Exploration by Self-supervised Prediction Pathak, Agrawal, Efros, and Darrell - UC Berkeley - ICML 2017.
ParameterNoise(action_space, *, framework, ...)	An exploration that changes a Model's parameters.

Inference

get_exploration_action(*, ...[, explore])

Returns a (possibly) exploratory action and its log-likelihood.

Callback hooks

before_compute_actions(*[, timestep, ...])	Hook for preparations before policy.compute_actions() is called.
on_episode_start(policy, *[, environment, ...])	Handles necessary exploration logic at the beginning of an episode.
on_episode_end(policy, *[, environment, ...])	Handles necessary exploration logic at the end of an episode.
postprocess_trajectory(policy, sample_batch)	Handles post-processing of done episode trajectories.

Setting and getting states

get_state([sess])	Returns the current exploration state.
set_state(state[, sess])	Sets the Exploration object's state to the given values.

Scheduler API

Use a scheduler to set scheduled values for variables (in Python, PyTorch, or TensorFlow) based on an (int64) timestep input. The computed values are usually float32 types.

Built-in Scheduler components

Schedule(framework)	Schedule classes implement various time-dependent scheduling schemas.
ConstantSchedule(value[, framework])	A Schedule where the value remains constant over time.
LinearSchedule(**kwargs)	Linear interpolation between initial_p and final_p.
PiecewiseSchedule(endpoints, float]], ...)	Implements a Piecewise Scheduler.
ExponentialSchedule(schedule_timesteps[, ...])	Exponential decay schedule from initial_p to final_p.
PolynomialSchedule(schedule_timesteps, ...)	Polynomial interpolation between initial_p and final_p.

Methods

value(t)	Generates the value given a timestep (based on schedule's logic).
__call__(t)	Simply calls self.value(t).

Training Operations Utilities

multi_gpu_train_one_step(algorithm, train_batch)	Multi-GPU version of train_one_step.
train_one_step(algorithm, train_batch[, ...])	Function that improves the all policies in train_batch on the local worker.

Framework Utilities

Import utilities

try_import_torch([error])	Tries importing torch and returns the module (or None).
try_import_tf([error])	Tries importing tf and returns the module (or None).
try_import_tfp([error])	Tries importing tfp and returns the module (or None).

Tensorflow utilities

explained_variance(y, pred)	Computes the explained variance for a pair of labels and predictions.
flatten_inputs_to_1d_tensor(inputs[, ...])	Flattens arbitrary input structs according to the given spaces struct.
get_gpu_devices()	Returns a list of GPU device names, e.g.
get_placeholder(*[, space, value, name, ...])	Returns a tf1.placeholder object given optional hints, such as a space.
huber_loss(x[, delta])	Computes the huber loss for a given term and delta parameter.
l2_loss(x)	Computes half the L2 norm over a tensor's values without the sqrt.
make_tf_callable(session_or_none[, ...])	Returns a function that can be executed in either graph or eager mode.
minimize_and_clip(optimizer, objective, var_list)	Computes, then clips gradients using objective, optimizer and var list.
one_hot(x, space)	Returns a one-hot tensor, given and int tensor and a space.
reduce_mean_ignore_inf(x[, axis])	Same as tf.reduce_mean() but ignores -inf values.
scope_vars(scope[, trainable_only])	Get variables inside a given scope.
warn_if_infinite_kl_divergence(policy, mean_kl)
zero_logps_from_actions(actions)	Helper function useful for returning dummy logp's (0) for some actions.

Torch utilities

apply_grad_clipping(policy, optimizer, loss)	Applies gradient clipping to already computed grads inside optimizer.
concat_multi_gpu_td_errors(policy)	Concatenates multi-GPU (per-tower) TD error tensors given TorchPolicy.
convert_to_torch_tensor(x[, device])	Converts any struct to torch.Tensors.
explained_variance(y, pred)	Computes the explained variance for a pair of labels and predictions.
flatten_inputs_to_1d_tensor(inputs[, ...])	Flattens arbitrary input structs according to the given spaces struct.
get_device(config)	Returns a torch device edepending on a config and current worker index.
global_norm(tensors)	Returns the global L2 norm over a list of tensors.
huber_loss(x[, delta])	Computes the huber loss for a given term and delta parameter.
l2_loss(x)	Computes half the L2 norm over a tensor's values without the sqrt.
minimize_and_clip(optimizer[, clip_val])	Clips grads found in optimizer.param_groups to given value in place.
one_hot(x, space)	Returns a one-hot tensor, given and int tensor and a space.
reduce_mean_ignore_inf(x[, axis])	Same as torch.mean() but ignores -inf values.
sequence_mask(lengths[, maxlen, dtype, ...])	Offers same behavior as tf.sequence_mask for torch.
warn_if_infinite_kl_divergence(policy, ...)
set_torch_seed([seed])	Sets the torch random seed to the given value.
softmax_cross_entropy_with_logits(logits, labels)	Same behavior as tf.nn.softmax_cross_entropy_with_logits.

Numpy utilities

aligned_array(*args, **kwargs)
concat_aligned(*args, **kwargs)
convert_to_numpy(x[, reduce_type, reduce_floats])	Converts values in stats to non-Tensor numpy or python types.
fc(x, weights[, biases, framework])	Calculates FC (dense) layer outputs given weights/biases and input.
flatten_inputs_to_1d_tensor(inputs[, ...])	Flattens arbitrary input structs according to the given spaces struct.
make_action_immutable(obj)	Flags actions immutable to notify users when trying to change them.
huber_loss(x[, delta])	Reference: https://en.wikipedia.org/wiki/Huber_loss.
l2_loss(x)	Computes half the L2 norm of a tensor (w/o the sqrt): sum(x**2) / 2.
lstm(x, weights[, biases, ...])	Calculates LSTM layer output given weights/biases, states, and input.
one_hot(x[, depth, on_value, off_value, dtype])	One-hot utility function for numpy.
relu(x[, alpha])	Implementation of the leaky ReLU function.
sigmoid(x[, derivative])	Returns the sigmoid function applied to x.
softmax(x[, axis, epsilon])	Returns the softmax values for x.