AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning
This work addresses efficiency issues for researchers and practitioners using deep reinforcement learning, though it appears incremental as it combines existing compression methods into a new framework.
The paper tackles the problem of high training time and energy consumption in deep reinforcement learning by proposing AcceRL, a lightweight parallel training framework that uses neural network compression to accelerate policy learning. The result shows that AcceRL reduces actor time by 2.0x to 4.13x and overall training time by 29.8% to 40.3% while maintaining policy quality.
Deep reinforcement learning has achieved great success in various fields with its super decision-making ability. However, the policy learning process requires a large amount of training time, causing energy consumption. Inspired by the redundancy of neural networks, we propose a lightweight parallel training framework based on neural network compression, AcceRL, to accelerate the policy learning while ensuring policy quality. Specifically, AcceRL speeds up the experience collection by flexibly combining various neural network compression methods. Overall, the AcceRL consists of five components, namely Actor, Learner, Compressor, Corrector, and Monitor. The Actor uses the Compressor to compress the Learner's policy network to interact with the environment. And the generated experiences are transformed by the Corrector with Off-Policy methods, such as V-trace, Retrace and so on. Then the corrected experiences are feed to the Learner for policy learning. We believe this is the first general reinforcement learning framework that incorporates multiple neural network compression techniques. Extensive experiments conducted in gym show that the AcceRL reduces the time cost of the actor by about 2.0 X to 4.13 X compared to the traditional methods. Furthermore, the AcceRL reduces the whole training time by about 29.8% to 40.3% compared to the traditional methods while keeps the same policy quality.