Using Mixture Density Networks to model continuous action priors

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2019

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Zwane, Sicelukwanda

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Abstract

Given enough data and access to an environment, model-free deep reinforcement learning algorithms allow for direct unsupervised behaviour learning in an environment without relying on a model of the environment's dynamics. As such, these algorithms have typically been favoured when learning tasks de ned in continuous environments such as robotics where such models are hard to come by. Despite their success here, these approaches still su er from high sample complexity and other drawbacks imposed by the use of the function approximators they rely on. This includes poor generalisation across tasks, poor initial performance and generally unsafe learning procedures due to inherent randomness in the learning process where actions sampled from random policies are executed for the purpose of exploration in the environment. In this work we address these issues by presenting a method for transferring knowledge from multiple, previously solved tasks (source tasks) to new target tasks in the same continuous environment. Equipping successive agents with this body of prior knowledge allows for easier acquisition of subsequent behaviours in the given continuous environment. We also empirically show that our proposed method \mixture density network action priors" demonstrates improved safety as well as improved learning compared to a standard model-free deep reinforcement learning agent in continuous environments.

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A dissertation submitted for the degree Master of Science March 2019

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