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Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback
Providing Reinforcement Learning (RL) agents with human feedback can dramatically improve various aspects of learning.
Fine-grained Sentiment Controlled Text Generation
DE-VAE achieves better control of sentiment as an attribute while preserving the content by learning a suitable lossless transformation network from the disentangled sentiment space to the desired entangled representation.
Deep Reinforcement Learning with Implicit Human Feedback
Building atop the baseline, we then make the following novel contributions in our work: (i) We argue that the definition of error-potentials is generalizable across different environments; specifically we show that error-potentials of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the error-potentials.
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