  Can an agent learn efficiently in a noisy and self adapting environment with
sequential, non-stationary and non-homogeneous observations? Through trading
bots, we illustrate how Deep Reinforcement Learning (DRL) can tackle this
challenge. Our contributions are threefold: (i) the use of contextual
information also referred to as augmented state in DRL, (ii) the impact of a
one period lag between observations and actions that is more realistic for an
asset management environment, (iii) the implementation of a new repetitive
train test method called walk forward analysis, similar in spirit to cross
validation for time series. Although our experiment is on trading bots, it can
easily be translated to other bot environments that operate in sequential
environment with regime changes and noisy data. Our experiment for an augmented
asset manager interested in finding the best portfolio for hedging strategies
shows that AAMDRL achieves superior returns and lower risk.
