  We proposed a new Portfolio Management method termed as Robust Log-Optimal
Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by
approximating the traditional objective function with quadratic Taylor
expansion. It avoids GLOS's complex CDF estimation process,hence resists the
"Butterfly Effect" caused by estimation error. Besides,RLOS retains GLOS's
profitability and the optimization problem involved in RLOS is computationally
far more practical compared to GLOS. Further, we combine RLOS with
Reinforcement Learning (RL) and propose the so-called Robust Log-Optimal
Strategy with Reinforcement Learning (RLOSRL), where the RL agent receives the
analyzed results from RLOS and observes the trading environment to make
comprehensive investment decisions. The RLOSRL's performance is compared to
some traditional strategies on several back tests, where we randomly choose a
selection of constituent stocks of the CSI300 index as assets under management
and the test results validate its profitability and stability.
