First Order Constrained Optimization in Policy Space

• URL: http://arxiv.org/abs/2002.06506v2
• Date: Sun, 25 Oct 2020 15:35:09 GMT
• Title: First Order Constrained Optimization in Policy Space
• Authors: Yiming Zhang, Quan Vuong, Keith W. Ross
• Abstract summary: We propose a novel approach called First Order Constrained Optimization in Policy Space (FOCOPS) FOCOPS maximizes an agent's overall reward while ensuring the agent satisfies a set of cost constraints. We provide empirical evidence that our simple approach achieves better performance on a set of constrained robotics locomotive tasks.
• Score: 19.00289722198614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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