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Found 4 papers, 2 papers with code
A Narration-based Reward Shaping Approach using Grounded Natural Language Commands
While this problem can be addressed through reward shaping, such approaches typically require a human expert with specialized knowledge.
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of {\em reward sparsity}.
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli.
Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces
While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data.
