# Deep Ordinal Reinforcement Learning

## TL;DR

This paper introduces a novel approach to reinforcement learning that uses ordinal rewards instead of numerical ones, adapting existing algorithms like Deep Q-Networks to this setting and demonstrating comparable or improved performance on benchmark tasks.

## Contribution

The paper presents a general method for adapting reinforcement learning algorithms to ordinal rewards, including the development of Ordinal Deep Q-Networks, and evaluates their effectiveness.

## Key findings

- Ordinal RL variants perform comparably to numerical ones on benchmark problems.
- Ordinal RL can yield better results with simpler reward signals.
- The approach is validated on OpenAI Gym environments.

## Abstract

Reinforcement learning usually makes use of numerical rewards, which have nice properties but also come with drawbacks and difficulties. Using rewards on an ordinal scale (ordinal rewards) is an alternative to numerical rewards that has received more attention in recent years. In this paper, a general approach to adapting reinforcement learning problems to the use of ordinal rewards is presented and motivated. We show how to convert common reinforcement learning algorithms to an ordinal variation by the example of Q-learning and introduce Ordinal Deep Q-Networks, which adapt deep reinforcement learning to ordinal rewards. Additionally, we run evaluations on problems provided by the OpenAI Gym framework, showing that our ordinal variants exhibit a performance that is comparable to the numerical variations for a number of problems. We also give first evidence that our ordinal variant is able to produce better results for problems with less engineered and simpler-to-design reward signals.