Randomized Value Functions via Multiplicative Normalizing Flows

Conference on Uncertainty in Artificial Intelligence (UAI)


Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike traditional point estimate methods, randomized value functions maintain a posterior distribution over action-space values. This prevents the agent’s behavior policy from prematurely exploiting early estimates and falling into local optima. In this work, we leverage recent advances in variational Bayesian neural networks and combine these with traditional Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG) to achieve randomized value functions for high-dimensional domains. In particular, we augment DQN and DDPG with multiplicative normalizing flows in order to track a rich approximate posterior distribution over the parameters of the value function. This allows the agent to perform approximate Thompson sampling in a computationally efficient manner via stochastic gradient methods. We demonstrate the benefits of our approach through an empirical comparison in high dimensional environments.

Related Publications

All Publications

MuDoCo: Corpus for Multidomain Coreference Resolution and Referring Expression Generation

Scott Martin, Shivani Poddar, Kartikeya Upasani

LREC - May 15, 2020

Emerging Cross-lingual Structure in Pretrained Language Models

Shijie Wu, Alexis Conneau, Haoran Li, Luke Zettlemoyer, Veselin Stoyanov

ACL - July 9, 2020

Open Source Evolutionary Structured Optimization

Jeremy Rapin, Pauline Bennet, Emmanuel Centeno, Daniel Haziza, Antoine Moreau, Olivier Teytaud

Evolutionary Computation Software Systems Workshop at ​GECCO - July 9, 2020

Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning

Tianyu Li, Nathan Lambert, Roberto Calandra, Franziska Meier, Akshara Rai

ICRA - June 1, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy