In this paper we present results for reinforcement learning trading systems that outperform the S&P 500 Stock Index over a 25-year test period, thus demonstrating the presence of predictable structure in US stock prices. The reinforcement learning algorithms compared here include our new recurrent reinforcement learning (RRL)

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This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem. The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning ...

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Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing.

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List Of Free Reinforcement Learning Courses/Resources Online 1. Reinforcement Learning Offered at Georgia Tech as CS 8803 2. Practical Reinforcement Learning 3. Reinforcement I have certain implementations of reinforcement learning problems. Feel free to reach out to me here. · GitHub.

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Jul 14, 2017 · The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock ...

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[6] brought the approach of combining deep learning with reinforcement learning to center-stage by demonstrating a convolutional neural network (CNN) We wanted to scale up this deep Q-learning approach to the more challenging reinforcement learning problem of driving a car autonomously in...