Lifelong Learning of Factored Policies via Policy Gradients

Jorge A Mendez, ERIC EATON

Student First Author: Yes

Keywords: lifelong learning, policy gradient learning, factored policies

Abstract: Policy gradient methods have shown success in learning continuous control policies for high-dimensional dynamical systems. A major downside of such methods is the amount of exploration they require before yielding high-performing policies. In a lifelong learning setting, in which an agent is faced with multiple consecutive tasks over its lifetime, reusing information from previously seen tasks can substantially accelerate the learning of new tasks. We provide a novel method for lifelong policy gradient learning that trains lifelong function approximators directly via policy gradients, allowing the agent to benefit from accumulated knowledge throughout the entire training process. We show empirically that our algorithm learns faster and converges to better policies than single-task and lifelong learning baselines, and completely avoids catastrophic forgetting on a variety of challenging domains.

TL;DR: We devise an algorithm for policy gradient learning of factored policies that accelerates the training of new tasks and completely avoids catastrophic forgetting.