FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging... (read more)

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Methods used in the Paper


METHOD TYPE
Dilated Convolution
Convolutions
Global Average Pooling
Pooling Operations
Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
SAC
Convolutions
Convolution
Convolutions
Weight Decay
Regularization
Clipped Double Q-learning
Off-Policy TD Control
Batch Normalization
Normalization
Target Policy Smoothing
Regularization
Adam
Stochastic Optimization
Dense Connections
Feedforward Networks
DDPG
Policy Gradient Methods
Experience Replay
Replay Memory
ReLU
Activation Functions
A2C
Policy Gradient Methods
Entropy Regularization
Regularization
TD3
Policy Gradient Methods
PPO
Policy Gradient Methods