MSRL: Distributed Reinforcement Learning with Dataflow Fragments

Huanzhou Zhu, Bo Zhao, Gang Chen, Weifeng Chen, Yijie Chen, Liang Shi, Yaodong Yang, Peter Pietzuch, Lei Chen

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

75 Lataukset (Pure)

Abstrakti

A wide range of reinforcement learning (RL) algorithms have been proposed, in which agents learn from interactions with a simulated environment. Executing such RL training loops is computationally expensive, but current RL systems fail to support the training loops of different RL algorithms efficiently on GPU clusters: they either hard-code algorithm-specific strategies for parallelization and distribution; or they accelerate only parts of the computation on GPUs (e.g., DNN policy updates). We observe that current systems lack an abstraction that decouples the definition of an RL algorithm from its strategy for distributed execution.

We describe MSRL, a distributed RL training system that uses the new abstraction of a fragmented dataflow graph (FDG) to execute RL algorithms in a flexible way. An FDG is a heterogenous dataflow representation of an RL algorithm, which maps functions from the RL training loop to independent parallel dataflow fragments. Fragments account for the diverse nature of RL algorithms: each fragment can execute on a different device through a low-level dataflow implementation, e.g., an operator graph of a DNN engine, a CUDA GPU kernel, or a multi-threaded CPU process. At deployment time, a distribution policy governs how fragments are mapped to devices, without requiring changes to the RL algorithm implementation. Our experiments show that MSRL exposes trade-offs between different execution strategies, while surpassing the performance of existing RL systems with fixed execution strategies.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 2023 USENIX Annual Technical Conference
KustantajaUSENIX -The Advanced Computing Systems Association
Sivut977-993
ISBN (elektroninen)978-1-939133-35-9
TilaJulkaistu - 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaUSENIX Annual Technical Conference - Boston, Yhdysvallat
Kesto: 10 heinäk. 202312 heinäk. 2023
https://www.usenix.org/conference/atc23

Conference

ConferenceUSENIX Annual Technical Conference
LyhennettäATC
Maa/AlueYhdysvallat
KaupunkiBoston
Ajanjakso10/07/202312/07/2023
www-osoite

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