Live Webinar: "Reinforcement Learning in the Presence of Nonstationary Variables" with Simon Oullette

Conventional reinforcement learning is difficult, perhaps impossible to use "as is" in the context of financial trading, due to the presence of time-varying coefficients and nonstationary variables in the data. Common machine learning techniques assume the data distributions to be stationary, which is almost always false in financial contexts.

This talk explains in detail the nature of this problem, with Python code examples, and then provides a solution based on generative modeling and Monte Carlo simulations in a Bayesian context. By using an imagination-augmented reinforcement learning agent, we are able to train the agent to act in an optimal way even on historically unseen values of these stochastic, nonstationary coefficients and variables.

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