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Effective Baselines for Multiple Object Rearrangement Planning in Partially Observable Mapped Environments
This requires efficient trade-offs between exploration of the environment and planning for rearrangement, which is challenging because of long-horizon nature of the problem.
An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments
Through our attention mechanism, our method generates a single feature representation of environment given its multiple views.
Attention-based Deep Reinforcement Learning for Multi-view Environments
In reinforcement learning algorithms, it is a common practice to account for only a single view of the environment to make the desired decisions; however, utilizing multiple views of the environment can help to promote the learning of complicated policies.
Long-term face tracking in the wild using deep learning
This paper investigates long-term face tracking of a specific person given his/her face image in a single frame as a query in a video stream.
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