Honeywell International Inc.

Nathan Lawrence

Gregory Edward Stewart

Philip D Loewen

Bhushan Gopaluni

A method and system for reinforcement learning can include an actor-critic framework comprising an actor and a critic, the actor comprising an actor network and the critic comprising a critic network; and a controller comprising a neural network embedded in the actor-critic framework and which can be tuned according to reinforcement learning based tuning including anti-windup tuning.

Method and system for directly tuning PID parameters using a simplified actor-critic approach to reinforcement learning

A method and system for reinforcement learning can involve applying a finite-difference approach to a controller, and tuning the controller in response to applying the finite-difference approach by taking a state as an entirety of a closed-loop step response. The disclosed finite-different approach is based on a random search to tuning the controller, which operates on the entire closed-loop step-response of the system and iteratively improves the gains towards a desired closed-loop response. This allows for prescribing stability requirement into the reward function without any modeling procedures.

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Vancouver, Canada

Nathan Lawrence