Ray AI Runtime
Monitoring and Debugging
Data ingest via either environment rollouts or other data-generating methods
(e.g. reading from offline files) is done in RLlib by WorkerSet
(together with other parallel RolloutWorkers) in the RLlib Algorithm
(under the self.workers property):
A typical RLlib WorkerSet setup inside an RLlib Algorithm: Each WorkerSet contains
exactly one local RolloutWorker object and n ray remote
RolloutWorker (ray actors).
The workers contain a policy map (with one or more policies), and - in case a simulator
(env) is available - a vectorized BaseEnv
(containing m sub-environments) and a SamplerInput (either synchronous or asynchronous) which controls
the environment data collection loop.
In the online (environment is available) as well as the offline case (no environment),
Algorithm uses the sample() method to
get SampleBatch objects for training.#