Researchers at Google Brain recently open-sourced their Scalable, Efficient Deep-RL (SEED RL) algorithm for AI reinforcement-learning. SEED RL is a distributed architecture that achieves state-of-the-art results on several RL benchmarks at lower cost and up to 80x faster than previous systems.
The team published a description of the SEED RL architecture and the results of several experiments in a paper accepted at the 2020 International Conference on Learning Representations (ICLR). The work addresses several drawbacks of existing distributed reinforcement-learning systems by moving neural-network inference to a central learner server, which can take advantage of GPU or TPU hardware accelerators.
