1. [1]Timothy P. Lillicrap et al. 2015. Continuous control with deep reinforcement learning. arXiv:1509.02971v6. Retrieved from https://arxiv.org/abs/1509.02971v6 [1]Timothy P. Lillicrap et al. 2015. Continuous control with deep reinforcement learning. arXiv:1509.02971v6. Retrieved from https://arxiv.org/abs/1509.02971v6
2. [2]Zhuoran Xiong et al. 2018. Practical deep reinforcement learning approach for stock trading. arXiv:1811.07522v2. Retrieved from https://arxiv.org/abs/1811.07522v2 [2]Zhuoran Xiong et al. 2018. Practical deep reinforcement learning approach for stock trading. arXiv:1811.07522v2. Retrieved from https://arxiv.org/abs/1811.07522v2
3. The complexity of the stock market
4. [4]Ryan Lowe et al. 2017. Multi-agent actor-critic for mixed cooperative-competitive environments. arXiv:1706.02275. Retrieved from https://arxiv.org/abs/1706.02275v4 [4]Ryan Lowe et al. 2017. Multi-agent actor-critic for mixed cooperative-competitive environments. arXiv:1706.02275. Retrieved from https://arxiv.org/abs/1706.02275v4
5. [5]Wenhang Bao and Xiao-yang Liu. 2019. Multi-agent deep reinforcement learning for liquidation strategy analysis. arXiv:1906.11046v1. Retrieved from https://arxiv.org/abs/1906.11046v1 [5]Wenhang Bao and Xiao-yang Liu. 2019. Multi-agent deep reinforcement learning for liquidation strategy analysis. arXiv:1906.11046v1. Retrieved from https://arxiv.org/abs/1906.11046v1