Towards Deployable RL -- What's Broken with RL Research and a Potential Fix

• URL: http://arxiv.org/abs/2301.01320v1
• Date: Tue, 3 Jan 2023 19:21:10 GMT
• Title: Towards Deployable RL -- What's Broken with RL Research and a Potential Fix
• Authors: Shie Mannor and Aviv Tamar
• Abstract summary: We point to some difficulties with current research which we feel are endemic to the direction taken by the community. To us, the current direction is not likely to lead to "deployable" RL: RL that works in practice and can work in practical situations yet still is economically viable.
• Score: 82.34145109359442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) has demonstrated great potential, but is currently full of overhyping and pipe dreams. We point to some difficulties with current research which we feel are endemic to the direction taken by the community. To us, the current direction is not likely to lead to "deployable" RL: RL that works in practice and can work in practical situations yet still is economically viable. We also propose a potential fix to some of the difficulties of the field.

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