# A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading

arXiv: https://arxiv.org/abs/2310.09462 · HTML: https://mlsanity.com/paper/2310.09462/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 74 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision](https://mlsanity.com/paper/1611.00020/) (2016)
- [An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2011.11235/) (2020)
- [Attention-Aware Face Hallucination via Deep Reinforcement Learning](https://mlsanity.com/paper/1708.03132/) (2017)
- [Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning](https://mlsanity.com/paper/2106.13386/) (2021)
- [Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting](https://mlsanity.com/paper/2209.05559/) (2022)
- [A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow](https://mlsanity.com/paper/1908.02750/) (2019)
- [Causal Modelling of Cryptocurrency Price Movements Using Discretisation-Aware Bayesian Networks](https://mlsanity.com/paper/2303.16148/) (2023)
- [Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships](https://mlsanity.com/paper/2306.08157/) (2023)

## Built on by (most cited first, 1 of 1)

- [Bayesian Network Fusion of Large Language Models for Sentiment Analysis](https://mlsanity.com/paper/2510.26484/) (2025)

## Abstract (arXiv)

Despite advances in artificial intelligence-enhanced trading methods, developing a profitable automated trading system remains challenging in the rapidly evolving cryptocurrency market. This research focuses on developing a reinforcement learning (RL) framework to tackle the complexities of trading five prominent altcoins: Binance Coin, Ethereum, Litecoin, Ripple, and Tether. To this end, we present the CausalReinforceNet~(CRN) framework, which integrates both Bayesian and dynamic Bayesian network techniques to empower the RL agent in trade decision-making. We develop two agents using the framework based on distinct RL algorithms to analyse performance compared to the Buy-and-Hold benchmark strategy and a baseline RL model. The results indicate that our framework surpasses both models in profitability, highlighting CRN's consistent superiority, although the level of effectiveness varies across different cryptocurrencies.