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Federated Deep Reinforcement Learning
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In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited. Despite the success of previous transfer learning approaches in deep reinforcement learning, directly transferring data or models from an agent to another agent is often not allowed due to the privacy of data and/or models in many privacy-aware applications. In this paper, we propose a novel deep reinforcement learning framework to federatively build models of high-quality for agents with consideration of their privacies, namely Federated deep Reinforcement Learning (FedRL). To protect the privacy of data and models, we exploit Gausian differentials on the information shared with each other when updating their local models. In the experiment, we evaluate our FedRL framework in two diverse domains, Grid-world and Text2Action domains, by comparing to various baselines.
Forward citations
Cited by 2 Pith papers
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Federated Reinforcement Learning in Heterogeneous Environments
FedRQ adds a robustness term to federated Q-learning and claims convergence to an optimal worst-case policy over heterogeneous local environments, but the proof has a reversed inequality.
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A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
A review of multi-agent reinforcement learning that catalogues federated, decentralized cooperative, and noncooperative regimes from the existing literature.
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