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Offline Reinforcement Learning from Real-World Demonstrations

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Summary

This article introduces Real-ORL, a framework for studying offline reinforcement learning with trajectories collected from real interactions. Rather than proposing a new learning algorithm, the cited work evaluates existing offline RL methods alongside behavior cloning on four robotic manipulation tasks. Its dataset uses expert-supervised, mostly successful demonstrations; tasks are divided into subgoals, and policies act in small steps toward them. The article reports experiments involving more than 3,000 training trajectories, more than 3,500 evaluation trajectories, and over 270 hours of human labor. The reported findings suggest that some heterogeneous data can improve learning when it provides overlapping state and action coverage, though results vary with the agent, task, and dataset.

The author then proposes applying the idea to trading by training on historical trade records from signals, using EURUSD data from the first seven months of 2023 as an example. This is an indirect and limited source of experience: records may omit stop-loss and take-profit details, and a small set of signal trades cannot capture all market risks. The author presents such data as a possible starting point for training that would need further refinement, not as a complete route to an optimal policy.

Key ideas

  • Offline reinforcement learning trains policies from stored interaction trajectories when new environment interaction is limited or costly.
  • Real-ORL evaluates existing offline RL algorithms and behavior cloning using expert-supervised demonstrations on four manipulation tasks.
  • Heterogeneous trajectories may help when they add state and action coverage, but their effect depends on the task, agent, and dataset.
  • Historical signal trades are proposed as a source of market experience, but they may omit risk details and require further policy refinement.

Tags

Machine learningForexRisk management

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.