We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL). By effectively utilizing modern accelerators, we show that it is not only possible to train on millions of frames per second but also to lower the cost of experiments compared to current methods. We achieve this with a simple architecture that features centralized inference and an optimized communication layer. SEED adopts two state of the art distributed algorithms, IMPALA/V-trace (policy gradients) and R2D2 (Q-learning), and is evaluated on Atari-57, DeepMind Lab and Google Research Football. We improve the state of the art on Football and are able to reach state of the art on Atari-57 three times faster in wall-time. For the scenarios we consider, a 40% to 80% cost reduction for running experiments is achieved. The implementation along with experiments is open-sourced so results can be reproduced and novel ideas tried out.
Github: anonymized.
This paper presents a scalable reinforcement learning training architecture which combines a number of modern engineering advances to address the inefficiencies of prior methods. The proposed architecture shows good performance on a wide variety of benchmarks from ALE to DeepMind Lab and Google Research Football. Important to the community, authors also open source their code and provide an estimate which shows that the proposed framework is cheaper to run on cloud platforms.
Pros:
1. This work is solid from the engineering perspective. It effectively addresses the problems with prior architectures and the accompanying source code is clear and well structured. It is also extensively tested on several RL benchmarks.
2. The proposed framework is especially suited for training large models as the model parameters are not transferred between actors and learners.
3. The paper is well written and organized.
Cons:
1. The gain of the main algorithmic improvement (SEED architecture) over the baseline (IMPALA architecture) is obscured by the usage of different hardware. TPUv3 has different characteristics than Nvidia P100/V100 GPU chips which also might contribute to the speed up.
Questions:
1. Is it possible to provide more “apple-to-apple” comparison by running SEED and IMPALA on the same hardware (TPUv3 or Nvidia P100/V100 GPU)?