EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning

Siddharth Aravindan, Dixant Mittal, Wee Sun Lee

Keywords: Exploration, Thompson Sampling, Model-Based Reinforcement Learning

Verify Author List: I have double-checked the author list and understand that additions and removals will not be allowed after the submission deadline.

TL;DR: This paper proposes variational distribution designs to approximate Thompson sampling for model based reinforcement learning in object based domains.

Abstract:

Posterior Sampling for Reinforcement Learning (PSRL) is a well-known algorithm that augments model-based reinforcement learning (MBRL) algorithms with Thompson sampling. PSRL maintains posterior distributions of the environment transition dynamics and the reward function, which are intractable for tasks with high-dimensional state and action spaces. Recent works show that dropout, used in conjunction with neural networks, induces variational distributions that can approximate these posteriors. In this paper, we propose Event-based Variational Distributions for Exploration (EVaDE), which are variational distributions that are useful for MBRL, especially when the underlying domain is object-based. We leverage the general domain knowledge of object-based domains to design three types of event-based convolutional layers to direct exploration. These layers rely on Gaussian dropouts and are inserted between the layers of the deep neural network model to help facilitate variational Thompson sampling. We empirically show the effectiveness of EVaDE-equipped Simulated Policy Learning (EVaDE-SimPLe) on the 100K Atari game suite.

Url Link To Your Supplementary Code: https://tinyurl.com/3zb8nywx

Primary Area: Deep Learning (architectures, deep reinforcement learning, generative models, deep learning theory, etc.)

Paper Checklist Guidelines: I certify that all co-authors of this work have read and commit to adhering to the guidelines in Call for Papers.

Student Author: No

Submission Number: 162