Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO. (arXiv:2206.10736v1 [cs.LG])

Web: http://arxiv.org/abs/2206.10736

A novel framework for solving the optimal execution and placement problems
using reinforcement learning (RL) with imitation was proposed. The RL agents
trained from the proposed framework consistently outperformed the industry
benchmark time-weighted average price (TWAP) strategy in execution cost and
showed great generalization across out-of-sample trading dates and tickers. The
impressive performance was achieved from three aspects. First, our RL network
architecture called Dual-window Denoise PPO enabled efficient learning in a
noisy market environment. Second, a reward scheme with …

arxiv lg