Suppose a fairly simple problem: You have to buy (resp sell) a given number of shares V in a fixed time horizon H with the aim to minimize your capital spent (resp maximize your revenue).
There are some research papers on the web that claim that using reiforcement learning algorithms can help make decision. See for example:

1/ Nevmyvaka and Kearns: Reinforcement Learning for Optimized Trade Execution 

These papers employ dynamic allocation strategies based either on LOB or bid ask spreads to do so. Contrary to the classical paper

2/Almgren and Chriss: Optimal Execution of Portfolio Transactions

they do not assume a the security prices dynamic from which they derive their strategy. Instead they use backtest results on a test set to measure the performance of their program. Of course this also supposes constraining hypothsesis on the Limit Order Book dynamics which are difficult to test.

My question is twofold : Do you know good research papers using Reinforcement Learning (or other Machine Learning method) for this problem ? By good I mean that the test set is large (not just a few days of backtest) and there is a real effort to be clear about the hypothesis and to have as little as possible.

Has any of you applied it in a live trading environment or know someone who did?