Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)
- Source
- doi.org
- Published
- Jul 20, 2017
Due to the safety risks and training sample inefficiency, it is often preferred to develop controllers in simulation. However, minor differences between the simulation and the real world can cause a significant sim-to-real gap. This gap can reduce the effec...
Alec Radford
Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)
doi.org
Info
- Published
- Jul 20, 2017
- Uploaded
- Jul 12, 2026
- Uploaded by
Trevor McFedries
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