Safe Reinforcement Learning by Imagining the Near Future
This work addresses safety in reinforcement learning for real-world applications, but it is incremental as it builds on existing model-based approaches.
The paper tackles safe reinforcement learning by penalizing unsafe trajectories in a model-based algorithm, achieving competitive rewards with fewer safety violations in continuous control tasks.
Safe reinforcement learning is a promising path toward applying reinforcement learning algorithms to real-world problems, where suboptimal behaviors may lead to actual negative consequences. In this work, we focus on the setting where unsafe states can be avoided by planning ahead a short time into the future. In this setting, a model-based agent with a sufficiently accurate model can avoid unsafe states. We devise a model-based algorithm that heavily penalizes unsafe trajectories, and derive guarantees that our algorithm can avoid unsafe states under certain assumptions. Experiments demonstrate that our algorithm can achieve competitive rewards with fewer safety violations in several continuous control tasks.