Understand
We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be violated.
- We show how to exploit the typically smooth dynamics of these systems and enable RL algorithms to never violate constraints during learning.
- Our technique is to directly add to the policy a safety layer that analytically solves an action correction formulation per each state.
- The novelty of obtaining an elegant closed-form solution is attained due to a linearized model, learned on past trajectories consisting of arbitrary actions.
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