  Dynamic Portfolio optimization is the process of distribution and rebalancing
of a fund into different financial assets such as stocks, cryptocurrencies,
etc, in consecutive trading periods to maximize accumulated profits or minimize
risks over a time horizon. This field saw huge developments in recent years,
because of the increased computational power and increased research in
sequential decision making through control theory. Recently Reinforcement
Learning(RL) has been an important tool in the development of sequential and
dynamic portfolio optimization theory. In this paper, we design a Deep
Reinforcement Learning(DRL) framework as an autonomous portfolio optimization
agent consisting of a Latent Feature State Space(LFSS) Module for filtering and
feature extraction of financial data which is used as a state space for deep RL
model. We develop an extensive RL agent with high efficiency and performance
advantages over several benchmarks and model-free RL agents used in prior work.
The noisy and non-stationary behaviour of daily asset prices in the financial
market is addressed through Kalman Filter. Autoencoders, ZoomSVD, and
restricted Boltzmann machines were the models used and compared in the module
to extract relevant time series features as state space. We simulate weekly
data, with practical constraints and transaction costs, on a portfolio of S&P
500 stocks. We introduce a new benchmark based on technical indicator Kd-Index
and Mean-Variance Model as compared to equal weighted portfolio used in most of
the prior work. The study confirms that the proposed RL portfolio agent with
state space function in the form of LFSS module gives robust results with an
attractive performance profile over baseline RL agents and given benchmarks.
