  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 imitation learning was
designed, and a comprehensive set of market features was studied. Third, our
flexible action formulation allowed the RL agent to tackle optimal execution
and placement collectively resulting in better performance than solving
individual problems separately. The RL agent's performance was evaluated in our
multi-agent realistic historical limit order book simulator in which price
impact was accurately assessed. In addition, ablation studies were also
performed, confirming the superiority of our framework.
