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Collaborative exploration and reinforcement learning between heterogeneously skilled agents in environments with sparse rewards

Published in International Joint Conference on Neural Networks, IJCNN, 2021

Abstract: A critical goal in Reinforcement Learning is the minimization of the time needed for an agent to learn to solve a given environment. In this context, collaborative reinforcement learning refers to the improvement of this learning process through the interaction between agents, which usually yields better results than training each agent in isolation. Most studies in this area have focused on the case with homogeneous agents, namely, agents equally skilled for undertaking their task. By contrast, heterogeneity among agents could arise due to the particular capabilities on how they sense the environment and/or the actions they could perform. Those differences eventually hinder the learning process and information sharing between agents. This issue becomes even more complicated to address over hard exploration scenarios where the extrinsic rewards collected from the environment are sparse. This work sheds light on the impact of leveraging collaborative learning strategies between heterogeneously skilled agents over hard exploration scenarios. Our study gravitates on how to share and exploit knowledge between the agents so as to mutually improve their learning procedures, further considering mechanisms to cope with sparse rewards. We assess the performance of these strategies via extensive simulations over modifications of the ViZDooM environment, which allow examining their benefits and drawbacks when dealing with agents endowed with different behavioral policies. Our results uncover the inherent problems of not considering the skill heterogeneity of the agents in the knowledge sharing strategy, and unleash a manifold of research directions aimed at circumventing these noted issues.

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Collaborative Training of Heterogeneous Reinforcement Learning Agents under Sparse Rewards

Published in Arxiv Preprint, 2022

Abstract: In the early stages of human life, babies develop their skills by exploring different scenarios motivated by their inherent satisfaction rather than by extrinsic rewards from the environment. This behavior, referred to as intrinsic motivation, has emerged as one solution to address the exploration challenge derived from reinforcement learning environments with sparse rewards. Diverse exploration approaches have been proposed to accelerate the learning process over single- and multi-agent problems with homogeneous agents. However, scarce studies have elaborated on collaborative learning frameworks between heterogeneous agents deployed into the same environment, but interacting with different instances of the latter without any prior knowledge. Beyond the heterogeneity, each agent’s characteristics grant access only to a subset of the full state space, which may hide different exploration strategies and optimal solutions. In this work we combine ideas from intrinsic motivation and transfer learning. Specifically, we focus on sharing parameters in actor-critic model architectures and on combining information obtained through intrinsic motivation with the aim of having a more efficient exploration and faster learning. We test our strategies through experiments performed over a modified ViZDooM’s My Way Home scenario, which is more challenging than its original version and allows evaluating the heterogeneity between agents. Our results reveal different ways in which a collaborative framework with little additional computational cost can outperform an independent learning process without knowledge sharing. Additionally, we depict the need for modulating correctly the importance between the extrinsic and intrinsic rewards to avoid undesired agent behaviors.

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IJCNN 2021


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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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