Hypernetworks in Meta-Reinforcement Learning
- URL: http://arxiv.org/abs/2210.11348v1
- Date: Thu, 20 Oct 2022 15:34:52 GMT
- Title: Hypernetworks in Meta-Reinforcement Learning
- Authors: Jacob Beck, Matthew Thomas Jackson, Risto Vuorio, Shimon Whiteson
- Abstract summary: Multi-task reinforcement learning (RL) and meta-RL aim to improve sample efficiency by generalizing over a distribution of related tasks.
State of the art methods often fail to outperform a degenerate solution that simply learns each task separately.
Hypernetworks are a promising path forward since they replicate the separate policies of the degenerate solution and are applicable to meta-RL.
- Score: 47.25270748922176
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Training a reinforcement learning (RL) agent on a real-world robotics task
remains generally impractical due to sample inefficiency. Multi-task RL and
meta-RL aim to improve sample efficiency by generalizing over a distribution of
related tasks. However, doing so is difficult in practice: In multi-task RL,
state of the art methods often fail to outperform a degenerate solution that
simply learns each task separately. Hypernetworks are a promising path forward
since they replicate the separate policies of the degenerate solution while
also allowing for generalization across tasks, and are applicable to meta-RL.
However, evidence from supervised learning suggests hypernetwork performance is
highly sensitive to the initialization. In this paper, we 1) show that
hypernetwork initialization is also a critical factor in meta-RL, and that
naive initializations yield poor performance; 2) propose a novel hypernetwork
initialization scheme that matches or exceeds the performance of a
state-of-the-art approach proposed for supervised settings, as well as being
simpler and more general; and 3) use this method to show that hypernetworks can
improve performance in meta-RL by evaluating on multiple simulated robotics
benchmarks.
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