Research map / Reinforcement learning / Visual and representation RL
Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add demonstration buffer (Dual Thrust demos) to RDPG (ablation) | RDPG · IF futures | total return: 6.96 -> 19.99 | Helped |
| add behavior cloning from hindsight expert with Q-filter to RDPG (ablation) | RDPG · IF futures | total return: 6.96 -> 28.34 | Helped |
| add behavior cloning on top of demonstration buffer (ablation) | RDPG-DB · IF futures | Sharpe ratio: 0.377 -> 0.842 | Helped |
| use recurrent GRU-based RDPG with imitation instead of feedforward DDPG | DRL trading agent · IF futures | total return: -16.36 -> 38.26 | Helped |
| learned iRDPG policy instead of static Dual Thrust rules | trading strategy · IF and IC futures | total return: IF 43.40 -> 38.26; IC -35.59 -> 24.73 | Mixed |
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