DeepMind have demonstrated amazing capabilities of a reinforcement machine learning agent to competently play Atari video games. It is most astounding that that during training nothing more than the image frames of the game and the score were provided to the
deep Q-network (DQN). The agent learned appropriate actions to accurately play a game and operate competently without any specific adaptions to the source code or network hyper-parameters. It simply needs training on a large number of game sequences to learn a new game.
Could this technology feasibly be adapted to permit a machine learning agent to take competent & appropriate trading actions in the financial markets? Just like playing Pong, but with the markets? A high score would be quite agreeable!
Does anyone have experience to articulate or advice on how this could be practically experimented upon?