Scalable Efficient Deep-RL

Author(s): Sherwin Chen

Originally published on Towards AI.

Deficiency of Traditional Distributed RL

Photo by Dawid Zawiła on Unsplash

Traditional scalable reinforcement learning framework, such as IMPALA and R2D2, runs multiple agents in parallel to collect transitions, each with its own copy of model from the parameter server(or learner). This architecture imposes high bandwidth requirements since they demand transfers of model parameters, environment information and etc. In this article, we discuss a modern scalable RL agent called SEED(), proposed by Espeholt&Marinier&Stanczyk et al in Google Brain team. that utilizes modern accelerators to speed up both data collection and learning process and lower the running cost(80% reduction against IMPALA measured on Google Cloud).

Comparison between SEED… Read the full blog for free on Medium.

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Published via Towards AI