1,278 research outputs found
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
In multi-agent reinforcement learning, discovering successful collective
behaviors is challenging as it requires exploring a joint action space that
grows exponentially with the number of agents. While the tractability of
independent agent-wise exploration is appealing, this approach fails on tasks
that require elaborate group strategies. We argue that coordinating the agents'
policies can guide their exploration and we investigate techniques to promote
such an inductive bias. We propose two policy regularization methods: TeamReg,
which is based on inter-agent action predictability and CoachReg that relies on
synchronized behavior selection. We evaluate each approach on four challenging
continuous control tasks with sparse rewards that require varying levels of
coordination as well as on the discrete action Google Research Football
environment. Our experiments show improved performance across many cooperative
multi-agent problems. Finally, we analyze the effects of our proposed methods
on the policies that our agents learn and show that our methods successfully
enforce the qualities that we propose as proxies for coordinated behaviors.Comment: 23 pages, 16 figures. This revised version contains additional
results and minor edit
An Inventory of Existing Neuroprivacy Controls
Brain-Computer Interfaces (BCIs) facilitate communication between brains and computers. As these devices become increasingly popular outside of the medical context, research interest in brain privacy risks and countermeasures has bloomed. Several neuroprivacy threats have been identified in the literature, including brain malware, personal data being contained in collected brainwaves and the inadequacy of legal regimes with regards to neural data protection. Dozens of controls have been proposed or implemented for protecting neuroprivacy, although it has not been immediately apparent what the landscape of neuroprivacy controls consists of. This paper inventories the implemented and proposed neuroprivacy risk mitigation techniques from open source repositories, BCI providers and the academic literature. These controls are mapped to the Hoepman privacy strategies and their implementation status is described. Several research directions for ensuring the protection of neuroprivacy are identified
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