Associated Links
McGill Mobile Robotics Lab / Reasoning & Learning Lab
Bayesian Policy Gradients via Alpha Divergence Dropout Inference

Using Bayesian Neural Networks via alpha divergences for value functions on HalfCheetah benchmark environment.
Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult. We propose an approach which instead estimates a distribution by fitting the value function with a Bayesian Neural Network. We optimize an $\alpha$-divergence objective with Bayesian dropout approximation to learn and estimate this distribution. We show that using the Monte Carlo posterior mean of the Bayesian value function distribution, rather than a deterministic network, improves stability and performance of policy gradient methods in continuous control MuJoCo simulations.