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Bayesian Exploration for Lifelong Reinforcement Learning
Haotian Fu · Shangqun Yu · Michael Littman · George Konidaris

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in Deep Reinforcement Learning »
Event URL: https://openreview.net/forum?id=ciln1V9r4Jj »

A central question in reinforcement learning (RL) is how to leverage prior knowledge to accelerate learning in new tasks. We propose a Bayesian exploration method for lifelong reinforcement learning (BLRL) that aims to learn a Bayesian posterior that distills the common structure shared across different tasks. We further derive a sample complexity analysis of BLRL in the finite MDP setting. To scale our approach, we propose a variational Bayesian Lifelong Learning (VBLRL) algorithm that is based on Bayesian neural networks, can be combined with recent model-based RL methods, and exhibits backward transfer. Experimental results on three challenging domains show that our algorithms adapt to new tasks faster than state-of-the-art lifelong RL methods.

Author Information

Haotian Fu (Brown University)
Shangqun Yu (Brown University)
Michael Littman (Brown University)
George Konidaris (Brown University)

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