9 Hurdles to Make Reinforcement Learning Work in the Real World
Reinforcement learning (RL) wins in games and simulators—but deploying it on real products is a different story. Gabriel Dulac‑Arnold, Daniel Mankowitz, and Todd Hester outline nine must-solve challenges before RL can safely power real-world systems.
- Safety & constraints: avoid harmful actions while learning.
- Sample efficiency: learn from limited, costly data.
- Non-stationarity: cope when users, markets, or sensors change.
- Partial observability: act with missing or delayed signals.
- Long horizons & credit: link actions to delayed outcomes.
- Latency & reliability: meet real-time and uptime needs.
- Exploration you can trust: try new things without breaking stuff.
- Transfer & generalization: work across tasks and drifts.
- Measurement: clear metrics for offline + online evaluation.
The authors also present a testbed that bakes in these pitfalls, encouraging practical solutions—not just leaderboard scores.
Paper: http://arxiv.org/abs/1904.12901
Paper: http://arxiv.org/abs/1904.12901v1
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