The reinforcement learning Kelly strategy
针对全凯利投资策略在实际中因估计误差而表现不佳的问题，本文提出基于强化学习的RL凯利策略，并通过模拟验证其优越性。
The full Kelly portfolio strategy's deficiency in the face of estimation errors in practice can be mitigated by fractional or shrinkage Kelly strategies. This paper provides an alternative, the RL Kelly strategy, based on a reinforcement learning (RL) framework. RL algorithms are developed for the practical implementation of the RL Kelly strategy. Extensive simulation studies are conducted, and the results confirm the superior performance of the RL Kelly strategies.