Note

Ray 2.10.0 introduces the alpha stage of RLlib’s “new API stack”. The team is currently transitioning algorithms, example scripts, and documentation to the new code base throughout the subsequent minor releases leading up to Ray 3.0.

See here for more details on how to activate and use the new API stack.

Learner (Alpha)#

Learner allows you to abstract the training logic of RLModules. It supports both gradient-based and non-gradient-based updates (e.g. polyak averaging, etc.) The API enables you to distribute the Learner using data- distributed parallel (DDP). The Learner achieves the following:

Facilitates gradient-based updates on RLModule.
Provides abstractions for non-gradient based updates such as polyak averaging, etc.
Reporting training statistics.
Checkpoints the modules and optimizer states for durable training.

The Learner class supports data-distributed- parallel style training using the LearnerGroup API. Under this paradigm, the LearnerGroup maintains multiple copies of the same Learner with identical parameters and hyperparameters. Each of these Learner instances computes the loss and gradients on a shard of a sample batch and then accumulates the gradients across the Learner instances. Learn more about data-distributed parallel learning in this article.

LearnerGroup also allows for asynchronous training and (distributed) checkpointing for durability during training.

Enabling Learner API in RLlib experiments#

Adjust the amount of resources for training using the num_gpus_per_learner, num_cpus_per_learner, and num_learners arguments in the AlgorithmConfig.

from ray.rllib.algorithms.ppo.ppo import PPOConfig

config = (
    PPOConfig()
    .learners(
        num_learners=0,  # Set this to greater than 1 to allow for DDP style updates.
        num_gpus_per_learner=0,  # Set this to 1 to enable GPU training.
        num_cpus_per_learner=1,
    )
)

Note

This features is in alpha. If you migrate to this algorithm, enable the feature by via AlgorithmConfig.api_stack(enable_rl_module_and_learner=True, enable_env_runner_and_connector_v2=True).

The following algorithms support Learner out of the box. Implement an algorithm with a custom Learner to leverage this API for other algorithms.

Algorithm	Supported Framework
PPO
Impala
APPO

Basic usage#

Use the LearnerGroup utility to interact with multiple learners.

Construction#

If you enable the RLModule and Learner APIs via the AlgorithmConfig, then calling build() constructs a LearnerGroup for you, but if you’re using these APIs standalone, you can construct the LearnerGroup as follows.

Contstructing a LearnerGroup

env = gym.make("CartPole-v1")

# Create an AlgorithmConfig object from which we can build the
# LearnerGroup.
config = (
    PPOConfig()
    # Number of Learner workers (Ray actors).
    # Use 0 for no actors, only create a local Learner.
    # Use >=1 to create n DDP-style Learner workers (Ray actors).
    .learners(num_learners=1)
    # Specify the learner's hyperparameters.
    .training(
        use_kl_loss=True,
        kl_coeff=0.01,
        kl_target=0.05,
        clip_param=0.2,
        vf_clip_param=0.2,
        entropy_coeff=0.05,
        vf_loss_coeff=0.5
    )
)

# Construct a new LearnerGroup using our config object.
learner_group = config.build_learner_group(env=env)

Constructing a Learner

env = gym.make("CartPole-v1")

# Create an AlgorithmConfig object from which we can build the
# Learner.
config = (
    PPOConfig()
    # Specify the Learner's hyperparameters.
    .training(
        use_kl_loss=True,
        kl_coeff=0.01,
        kl_target=0.05,
        clip_param=0.2,
        vf_clip_param=0.2,
        entropy_coeff=0.05,
        vf_loss_coeff=0.5
    )
)
# Construct a new Learner using our config object.
learner = config.build_learner(env=env)

# Needs to be called on the learner before calling any functions.
learner.build()

Updates#

Updating a LearnerGroup

TIMESTEPS = {"num_env_steps_sampled_lifetime": 250}

# This is a blocking update.
results = learner_group.update_from_batch(batch=DUMMY_BATCH, timesteps=TIMESTEPS)

# This is a non-blocking update. The results are returned in a future
# call to `update_from_batch(..., async_update=True)`
_ = learner_group.update_from_batch(batch=DUMMY_BATCH, async_update=True, timesteps=TIMESTEPS)

# Artificially wait for async request to be done to get the results
# in the next call to
# `LearnerGroup.update_from_batch(..., async_update=True)`.
time.sleep(5)
results = learner_group.update_from_batch(
    batch=DUMMY_BATCH, async_update=True, timesteps=TIMESTEPS
)
# `results` is a list of n items (where n is the number of async results collected).
assert isinstance(results, list), results
# Each item in that list is another list of m items (where m is the number of Learner
# workers).
assert isinstance(results[0], list), results
# Each item in the inner list is a result dict from the Learner worker.
assert isinstance(results[0][0], dict), results

When updating a LearnerGroup you can perform blocking or async updates on batches of data. Async updates are necessary for implementing async algorithms such as APPO/IMPALA.

Updating a Learner

# This is a blocking update (given a training batch).
result = learner.update_from_batch(batch=DUMMY_BATCH, timesteps=TIMESTEPS)

When updating a Learner you can only perform blocking updates on batches of data. You can perform non-gradient based updates before or after the gradient-based ones by overriding before_gradient_based_update() and after_gradient_based_update().

Getting and setting state#

Getting and Setting State for a LearnerGroup

# Get the LearnerGroup's RLModule weights and optimizer states.
state = learner_group.get_state()
learner_group.set_state(state)

# Only get the RLModule weights.
weights = learner_group.get_weights()
learner_group.set_weights(weights)

Set/get the state dict of all learners through learner_group through LearnerGroup.set_state or LearnerGroup.get_state. This includes the neural network weights and the optimizer states on each learner. For example an Adam optimizer’s state has momentum information based on recently computed gradients. If you only want to get or set the weights of the RLModules (neural networks) of all Learners, you can do so through the LearnerGroup APIs LearnerGroup.get_weights and LearnerGroup.set_weights.

Getting and Setting State for a Learner

from ray.rllib.core import COMPONENT_RL_MODULE

# Get the Learner's RLModule weights and optimizer states.
state = learner.get_state()
# Note that `state` is now a dict:
# {
#    COMPONENT_RL_MODULE: [RLModule's state],
#    COMPONENT_OPTIMIZER: [Optimizer states],
# }
learner.set_state(state)

# Only get the RLModule weights (as numpy, not torch/tf).
rl_module_only_state = learner.get_state(components=COMPONENT_RL_MODULE)
# Note that `rl_module_only_state` is now a dict:
# {COMPONENT_RL_MODULE: [RLModule's state]}
learner.module.set_state(rl_module_only_state)

You can set and get the entire state of a Learner using set_state() and get_state() . For getting only the RLModule’s weights (without optimizer states), use the components=COMPONENT_RL_MODULE arg in get_state() (see code above). For setting only the RLModule’s weights (without touching the optimizer states), use get_state() and pass in a dict: {COMPONENT_RL_MODULE: [RLModule's state]} (see code above).

Checkpointing#

Checkpointing a LearnerGroup

learner_group.save_to_path(LEARNER_GROUP_CKPT_DIR)
learner_group.restore_from_path(LEARNER_GROUP_CKPT_DIR)

Checkpoint the state of all learners in the LearnerGroup through save_to_path() and restore the state of a saved LearnerGroup through restore_from_path(). A LearnerGroup’s state includes the neural network weights and all optimizer states. Note that since the state of all of the Learner instances is identical, only the states from the first Learner are saved.

Checkpointing a Learner

learner.save_to_path(LEARNER_CKPT_DIR)
learner.restore_from_path(LEARNER_CKPT_DIR)

Checkpoint the state of a Learner through save_to_path() and restore the state of a saved Learner through restore_from_path(). A Learner’s state includes the neural network weights and all optimizer states.

Implementation#

Learner has many APIs for flexible implementation, however the core ones that you need to implement are:

Method	Description
`configure_optimizers_for_module()`	set up any optimizers for a RLModule.
`compute_loss_for_module()`	calculate the loss for gradient based update to a module.
`before_gradient_based_update()`	do any non-gradient based updates to a RLModule before(!) the gradient based ones, e.g. add noise to your network.
`after_gradient_based_update()`	do any non-gradient based updates to a RLModule after(!) the gradient based ones, e.g. update a loss coefficient based on some schedule.

Starter Example#

A Learner that implements behavior cloning could look like the following:

class BCTorchLearner(TorchLearner):

    @override(Learner)
    def compute_loss_for_module(
        self,
        *,
        module_id: ModuleID,
        config: AlgorithmConfig = None,
        batch: Dict[str, Any],
        fwd_out: Dict[str, TensorType],
    ) -> TensorType:

        # standard behavior cloning loss
        action_dist_inputs = fwd_out[SampleBatch.ACTION_DIST_INPUTS]
        action_dist_class = self._module[module_id].get_train_action_dist_cls()
        action_dist = action_dist_class.from_logits(action_dist_inputs)
        loss = -torch.mean(action_dist.logp(batch[SampleBatch.ACTIONS]))

        return loss