Computer Science > Machine Learning
[Submitted on 23 Feb 2021 (v1), last revised 19 Jun 2021 (this version, v3)]
Title:Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL
View PDFAbstract:Actors and critics in actor-critic reinforcement learning algorithms are functionally separate, yet they often use the same network architectures. This case study explores the performance impact of network sizes when considering actor and critic architectures independently. By relaxing the assumption of architectural symmetry, it is often possible for smaller actors to achieve comparable policy performance to their symmetric counterparts. Our experiments show up to 99% reduction in the number of network weights with an average reduction of 77% over multiple actor-critic algorithms on 9 independent tasks. Given that reducing actor complexity results in a direct reduction of run-time inference cost, we believe configurations of actors and critics are aspects of actor-critic design that deserve to be considered independently, particularly in resource-constrained applications or when deploying multiple actors simultaneously.
Submission history
From: Siddharth Mysore [view email][v1] Tue, 23 Feb 2021 19:07:47 UTC (3,753 KB)
[v2] Thu, 22 Apr 2021 16:16:30 UTC (944 KB)
[v3] Sat, 19 Jun 2021 02:51:19 UTC (935 KB)
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