# Deep Reinforcement Learning for Asset Allocation: Reward Clipping

arXiv: https://arxiv.org/abs/2301.05300 · HTML: https://mlsanity.com/paper/2301.05300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Hybrid Reward Architecture for Reinforcement Learning](https://mlsanity.com/paper/1706.04208/) (2017)
- [Asset Allocation: From Markowitz to Deep Reinforcement Learning](https://mlsanity.com/paper/2208.07158/) (2022)

## Abstract (arXiv)

Recently, there are many trials to apply reinforcement learning in asset allocation for earning more stable profits. In this paper, we compare performance between several reinforcement learning algorithms - actor-only, actor-critic and PPO models. Furthermore, we analyze each models' character and then introduce the advanced algorithm, so called Reward clipping model. It seems that the Reward Clipping model is better than other existing models in finance domain, especially portfolio optimization - it has strength both in bull and bear markets. Finally, we compare the performance for these models with traditional investment strategies during decreasing and increasing markets.