Deep reinforcement learning for time series: playing idealized trading games
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Summary
Deep Q-learning is investigated as an end-to-end solution to estimate the optimal strategies for acting on time series input to test whether the agent can capture the underlying dynamics and utilize the hidden relation among the inputs.
- Type
- preprint
- Published
- 2018-03-11
- Cited by
- 16
- References
- 21
- Access
- Open access
- OpenAlex
- https://openalex.org/W2792007219
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3836393
Keywords
Univariate, Bivariate analysis, Reinforcement learning, Series (stratigraphy), Computer science
References
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- A Recurrent Latent Variable Model for Sequential Data
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Playing Atari with Deep Reinforcement Learning
- Gated Feedback Recurrent Neural Networks
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Visualizing and Understanding Recurrent Networks
- Long Short-Term Memory
- Learning to Forget: Continual Prediction with LSTM
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- ImageNet classification with deep convolutional neural networks
- Multi-Scale Convolutional Neural Networks for Time Series Classification
- Hierarchical Multiscale Recurrent Neural Networks
- Time series classification from scratch with deep neural networks: A strong baseline
- Comparative Study of CNN and RNN for Natural Language Processing
- Conditional Time Series Forecasting with Convolutional Neural Networks
- Mastering the game of Go without human knowledge
- Long Short Term Memory Networks for Anomaly Detection in Time Series
- Learning to Diagnose with LSTM Recurrent Neural Networks
- Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks
Cited by
- A quantitative trading method using deep convolution neural network
- Deep Learning for Financial Applications : A Survey
- Implementing action mask in proximal policy optimization (PPO) algorithm
- Trading ETFs with Deep Q-Learning Algorithm
- A tabular sarsa-based stock market agent
- An Improved Reinforcement Learning Model Based on Sentiment Analysis
- Research on investment strategies of stock market based on sentiment indicators and deep reinforcement learning
- Intelligent Video Ingestion for Real-time Traffic Monitoring
- Applying Value-Based Deep Reinforcement Learning on KPI Time Series Anomaly Detection
- Deep Reinforcement Learning for Online Error Detection in Cyber-Physical Systems
- DeepDLP: Deep Reinforcement Learning based Framework for Dynamic Liner Trade Pricing
- Reinforcement Learning for the Face Support Pressure of Tunnel Boring Machines
- Deep Reinforcement Learning Algorithms for Dynamic Portfolio Optimization Under Time-Varying Market and Risk Constraints
- Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility
- Reinforcement Learning in Quantitative Trading: A Survey
- Improved Method of Stock Trading under Reinforcement Learning Based on DRQN and Sentiment Indicators ARBR
- Piano Fingering with Reinforcement Learning
- Learning Control Tasks Via Output Measurements: A Data-Efficient Iterative Online Approach
- Reinforcement Learning in Quantitative Trading: A Survey
- Reinforcement learning for the face support pressure of tunnel boring machines
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