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Poster
First Order Constrained Optimization in Policy Space
Yiming Zhang · Quan Vuong · Keith Ross

Mon Dec 07 09:00 PM -- 11:00 PM (PST) @ Poster Session 0 #87

In reinforcement learning, an agent attempts to learn high-performing behaviors through interacting with the environment, such behaviors are often quantified in the form of a reward function. However some aspects of behavior—such as ones which are deemed unsafe and to be avoided—are best captured through constraints. We propose a novel approach called First Order Constrained Optimization in Policy Space (FOCOPS) which maximizes an agent's overall reward while ensuring the agent satisfies a set of cost constraints. Using data generated from the current policy, FOCOPS first finds the optimal update policy by solving a constrained optimization problem in the nonparameterized policy space. FOCOPS then projects the update policy back into the parametric policy space. Our approach has an approximate upper bound for worst-case constraint violation throughout training and is first-order in nature therefore simple to implement. We provide empirical evidence that our simple approach achieves better performance on a set of constrained robotics locomotive tasks.

Author Information

Yiming Zhang (New York University)
Quan Vuong (University of California, San Diego)
Keith Ross (NYU Shanghai)

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