Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.
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Resources for developers using Python for scientific computing and quantitative analysis
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Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.
under Machine Learning
Since the advent of deep reinforcement learning for game play in 2013, and simulated robotic control shortly after, a multitude of new algorithms have flourished. Most of these are model-free algorithms which can be categorized into three families: deep Q-learning, policy gradients, and Q-value policy gradients.
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