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seed_rl
Discontinued SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
I've been doing some tests to find the most efficient configuration for training using Google Cloud AI Platform. The results are here (note that "step" in this case represents a single sample/observation/frame from a single environment; iteration represents running the minimization function on a single batch). The results are a bit strange. I was under the assumption that training with TPUs would be one of the most efficient ways to train, but instead it's the least efficient by a wide margin. I'm using Google Research's SEED RL codebase, so I'm assuming there are no bugs in my code.
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