MIT researchers have developed a groundbreaking technique to tackle complex stabilize-avoid problems, surpassing existing methods in safety and stability. By reframing the challenge as a constrained optimization problem and employing deep reinforcement learning algorithms, the researchers achieved a tenfold increase in stability while ensuring the agent avoids obstacles.
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