ConnectorV2 and ConnectorV2 pipelines#

RLlib stores and transports all trajectory data in the form of SingleAgentEpisode or MultiAgentEpisode objects. Connector pipelines are the components that translate this episode data into tensor batches readable by neural network models right before the model forward pass.

../_images/generic_connector_pipeline.svg — **Generic ConnectorV2 Pipeline**: All pipelines consist of one or more `ConnectorV2` pieces. When calling the pipeline, you pass in a list of Episodes, the `RLModule` instance, and a batch, which initially might be an empty dict. Each `ConnectorV2` piece in the pipeline takes its predecessor’s output, starting on the left side with the batch, performs some transformations on the episodes, the batch, or both, and passes everything on to the next piece. Thereby, all `ConnectorV2` pieces can read from and write to the provided episodes, add any data from these episodes to the batch, or change the data that’s already in the batch. The pipeline then returns the output batch of the last piece.#

Note

Note that the batch output of the pipeline lives only as long as the succeeding RLModule forward pass or Env.step() call. RLlib discards the data afterwards. The list of episodes, however, may persist longer. For example, if a env-to-module pipeline reads an observation from an episode, mutates that observation, and then writes it back into the episode, the subsequent module-to-env pipeline is able to see the changed observation. Also, the Learner pipeline operates on the same episodes that have already passed through both env-to-module and module-to-env pipelines and thus might have undergone changes.

Three ConnectorV2 pipeline types#

There are three different types of connector pipelines in RLlib:

Env-to-module pipeline, which creates tensor batches for action computing forward passes.
Module-to-env pipeline (documentation pending), which translates a model’s output into RL environment actions.
Learner connector pipeline, which creates the train batch for a model update.

The ConnectorV2 API is an extremely powerful tool for customizing your RLlib experiments and algorithms. It allows you to take full control over accessing, changing, and re-assembling the episode data collected from your RL environments or your offline RL input files as well as controlling the exact nature and shape of the tensor batches that RLlib feeds into your models for computing actions or losses.

../_images/location_of_connector_pipelines_in_rllib.svg — **ConnectorV2 Pipelines**: Connector pipelines convert episodes into batched data, which your model can process (env-to-module and Learner) or convert your model’s output into action batches, which your possibly vectorized RL environment needs for stepping (module-to-env). The env-to-module pipeline, located on an `EnvRunner`, takes a list of episodes as input and outputs a batch for an `RLModule` forward pass that computes the next action. The module-to-env pipeline on the same `EnvRunner` takes the output of that `RLModule` and converts it into actions for the next call to your RL environment’s `step()` method. Lastly, a Learner connector pipeline, located on a `Learner` worker, converts a list of episodes into a train batch for the next `RLModule` update.#

The succeeding pages discuss the three pipeline types in more detail, however, all three have in common:

All connector pipelines are sequences of one or more ConnectorV2 pieces. You can nest these as well, meaning some of the pieces may be connector pipelines themselves.
All connector pieces and -pipelines are Python callables, overriding the __call__() method.
The call signatures are uniform across the different pipeline types. The main, mandatory arguments are the list of episodes, the batch to-be-built, and the RLModule instance. See the __call__() method for more details.
All connector pipelines can read from and write to the provided list of episodes as well as the batch and thereby perform data transforms as required.

Batch construction phases and formats#

When you push a list of input episodes through a connector pipeline, the pipeline constructs a batch from the given data. This batch always starts as an empty python dictionary and undergoes different formats and phases while passing through the different pieces of the pipeline.

The following applies to all env-to-module and learner connector pipelines (documentation in progress).

../_images/pipeline_batch_phases_single_agent.svg — **Batch construction phases and formats**: In the standard single-agent case, where only one ModuleID (`DEFAULT_MODULE_ID`) exists, the batch starts as an empty dictionary (left) and then undergoes a “collect data” phase, in which connector pieces add individual items to the batch by storing them under a) the column name, for example `obs` or `rewards`, and b) under the episode ID, from which they extracted the item. In most cases, your custom connector pieces operate during this phase. Once all custom pieces have performed their data insertions and transforms, the `AgentToModuleMapping` default piece performs a “reorganize by ModuleID” operation (center), during which the batch’s dictionary hierarchy changes to having the ModuleID (`DEFAULT_MODULE_ID`) at the top level and the column names thereunder. On the lowest level in the batch, data items still reside in python lists. Finally, the `BatchIndividualItems` default piece creates NumPy arrays out of the python lists, thereby batching all data (right).#

For multi-agent setups, where there are more than one ModuleIDs the AgentToModuleMapping default connector piece makes sure that the constructed output batch maps module IDs to the respective module’s forward batch:

../_images/pipeline_batch_phases_multi_agent.svg — **Batch construction for multi-agent**: In a multi-agent setup, the default `AgentToModuleMapping` connector piece reorganizes the batch by `ModuleID`, then column names, such that a `MultiRLModule` can loop through its sub-modules and provide each with a batch for the forward pass.#

RLlib’s MultiRLModule can split up the forward passes into individual submodules’ forward passes using the individual batches under the respective ModuleIDs. See here for how to write your own multi-module or multi-agent forward logic and override this default behavior of MultiRLModule.

Finally, if you have a stateful RLModule, for example an LSTM, RLlib adds two additional default connector pieces to the pipeline, AddTimeDimToBatchAndZeroPad and AddStatesFromEpisodesToBatch:

../_images/pipeline_batch_phases_single_agent_w_states.svg — **Batch construction for stateful models**: For stateful `RLModule` instances, RLlib automatically adds additional two default connector pieces to the pipeline. The `AddTimeDimToBatchAndZeroPad` piece converts all lists of individual data items on the lowest batch level into sequences of a fixed length (`max_seq_len`, see note below for how to set this) and automatically zero-pads these if it encounters an episode end. The `AddStatesFromEpisodesToBatch` piece adds the previously generated `state_out` values of your `RLModule` under the `state_in` column name to the batch. Note that RLlib only adds the `state_in` values for the first timestep in each sequence and therefore also doesn’t add a time dimension to the data in the `state_in` column.#

Note

To change the zero-padded sequence length for the AddTimeDimToBatchAndZeroPad connector, set in your config for custom models:

config.rl_module(model_config={"max_seq_len": ...})

And for RLlib’s default models:

from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig

config.rl_module(model_config=DefaultModelConfig(max_seq_len=...))