Gaussian Process Policy Optimization

• URL: http://arxiv.org/abs/2003.01074v1
• Date: Mon, 2 Mar 2020 18:06:27 GMT
• Title: Gaussian Process Policy Optimization
• Authors: Ashish Rao, Bidipta Sarkar, and Tejas Narayanan
• Abstract summary: We propose a novel actor-critic, model-free reinforcement learning algorithm. It employs a Bayesian method of parameter space exploration to solve environments. It is shown to be comparable to and at times empirically outperform current algorithms on environments that simulate robotic locomotion.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel actor-critic, model-free reinforcement learning algorithm which employs a Bayesian method of parameter space exploration to solve environments. A Gaussian process is used to learn the expected return of a policy given the policy's parameters. The system is trained by updating the parameters using gradient descent on a new surrogate loss function consisting of the Proximal Policy Optimization 'Clipped' loss function and a bonus term representing the expected improvement acquisition function given by the Gaussian process. This new method is shown to be comparable to and at times empirically outperform current algorithms on environments that simulate robotic locomotion using the MuJoCo physics engine.

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