A* Tree Search for Portfolio Management
  We propose a planning-based method to teach an agent to manage portfolio from
scratch. Our approach combines deep reinforcement learning techniques with
search techniques like AlphaGo. By uniting the advantages in A* search
algorithm with Monte Carlo tree search, we come up with a new algorithm named
A* tree search in which best information is returned to guide next search.
Also, the expansion mode of Monte Carlo tree is improved for a higher
utilization of the neural network. The suggested algorithm can also optimize
non-differentiable utility function by combinatorial search. This technique is
then used in our trading system. The major component is a neural network that
is trained by trading experiences from tree search and outputs prior
probability to guide search by pruning away branches in turn. Experimental
results on simulated and real financial data verify the robustness of the
proposed trading system and the trading system produces better strategies than
several approaches based on reinforcement learning.
