Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization

• URL: http://arxiv.org/abs/2310.06903v2
• Date: Sun, 14 Jul 2024 15:56:37 GMT
• Title: Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization
• Authors: Fan Yang, Wenxuan Zhou, Zuxin Liu, Ding Zhao, David Held,
• Abstract summary: This work introduces a novel approach that combines RL with trajectory optimization to manage this trade-off effectively. Our approach embeds safety constraints within the action space of a modified Markov Decision Process (MDP) The novel approach excels in its performance on challenging Safety Gym tasks, achieving significantly higher rewards and near-zero safety violations during inference.
• Score: 42.258173057389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safe Reinforcement Learning (RL) plays an important role in applying RL algorithms to safety-critical real-world applications, addressing the trade-off between maximizing rewards and adhering to safety constraints. This work introduces a novel approach that combines RL with trajectory optimization to manage this trade-off effectively. Our approach embeds safety constraints within the action space of a modified Markov Decision Process (MDP). The RL agent produces a sequence of actions that are transformed into safe trajectories by a trajectory optimizer, thereby effectively ensuring safety and increasing training stability. This novel approach excels in its performance on challenging Safety Gym tasks, achieving significantly higher rewards and near-zero safety violations during inference. The method's real-world applicability is demonstrated through a safe and effective deployment in a real robot task of box-pushing around obstacles.

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