Name
Title | ||
|---|---|---|
Improving Deep Reinforcement Learning for Financial Trading Using Neural Network Distillation |
Abstract | ||
|---|---|---|
Deep Reinforcement Learning (RL) is increasingly used for developing financial trading agents for a wide range of tasks. However, optimizing deep RL agents is known to be notoriously difficult and unstable, hindering the performance of financial trading agents. In this work, we propose a novel method for training deep RL agents, leading to better performing and more efficient RL agents. The proposed method works by first training a large and complex deep RL agent and then transferring the knowledge into a smaller and more efficient agent using neural network distillation. The ability of the proposed method to significantly improve deep RL for financial trading is demonstrated using experiments on a time series dataset consisting of Foreign Exchange (FOREX) trading pairs prices. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MLSP49062.2020.9231849 | 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | DocType | ISSN |
Deep Reinforcement Learning,Financial Markets,Trading | Conference | 1551-2541 |
ISBN | Citations | PageRank |
978-1-7281-6663-6 | 1 | 0.38 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Avraam Tsantekidis | 1 | 34 | 5.41 |
| N. Passalis | 2 | 117 | 33.70 |
| Anastasios Tefas | 3 | 2055 | 177.05 |