# Achieving Stable Training of Reinforcement Learning Agents in Bimodal Environments through Batch Learning

arXiv: https://arxiv.org/abs/2307.00923 · HTML: https://mlsanity.com/paper/2307.00923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning](https://mlsanity.com/paper/2010.03950/) (2020)

## Abstract (arXiv)

Bimodal, stochastic environments present a challenge to typical Reinforcement Learning problems. This problem is one that is surprisingly common in real world applications, being particularly applicable to pricing problems. In this paper we present a novel learning approach to the tabular Q-learning algorithm, tailored to tackling these specific challenges by using batch updates. A simulation of pricing problem is used as a testbed to compare a typically updated agent with a batch learning agent. The batch learning agents are shown to be both more effective than the typically-trained agents, and to be more resilient to the fluctuations in a large stochastic environment. This work has a significant potential to enable practical, industrial deployment of Reinforcement Learning in the context of pricing and others.