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

01/03/2023
by   Shie Mannor, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/28/2021

Reinforcement Learning for Quantitative Trading

Quantitative trading (QT), which refers to the usage of mathematical mod...
research
12/10/2020

Flatland-RL : Multi-Agent Reinforcement Learning on Trains

Efficient automated scheduling of trains remains a major challenge for m...
research
02/24/2018

Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari

Evolution Strategies (ES) have recently been demonstrated to be a viable...
research
09/13/2019

Towards an Adaptive Robot for Sports and Rehabilitation Coaching

The work presented in this paper aims to explore how, and to what extent...
research
12/28/2017

Reinforcement Learning with Analogical Similarity to Guide Schema Induction and Attention

Research in analogical reasoning suggests that higher-order cognitive fu...
research
08/26/2020

Identifying Critical States by the Action-Based Variance of Expected Return

The balance of exploration and exploitation plays a crucial role in acce...
research
03/03/2022

On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency

This thesis rigorously studies fundamental reinforcement learning (RL) m...

Please sign up or login with your details

Forgot password? Click here to reset