Related papers: Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models

Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models

URL: http://arxiv.org/abs/2102.08291v1
Date: Tue, 16 Feb 2021 17:21:55 GMT
Title: Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models
Authors: Qi Wang, Herke van Hoof
Abstract summary: We show that our model, called a graph structured surrogate model (GSSM), outperforms state-of-the-art methods in predicting environment dynamics. Our approach is able to obtain high returns, while allowing fast execution during deployment by avoiding test time policy gradient optimization.
Score: 40.08137765886609
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Reinforcement learning is a promising paradigm for solving sequential decision-making problems, but low data efficiency and weak generalization across tasks are bottlenecks in real-world applications. Model-based meta reinforcement learning addresses these issues by learning dynamics and leveraging knowledge from prior experience. In this paper, we take a closer look at this framework, and propose a new Thompson-sampling based approach that consists of a new model to identify task dynamics together with an amortized policy optimization step. We show that our model, called a graph structured surrogate model (GSSM), outperforms state-of-the-art methods in predicting environment dynamics. Additionally, our approach is able to obtain high returns, while allowing fast execution during deployment by avoiding test time policy gradient optimization.

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