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Found 2 papers, 1 papers with code
Deep Reinforcement Learning with Feedback-based Exploration
We employ binary corrective feedback as a general and intuitive manner to incorporate human intuition and domain knowledge in model-free machine learning.
Learning Gaussian Policies from Corrective Human Feedback
We demonstrate that the novel algorithm outperforms the current state-of-the-art in final performance, convergence rate and robustness to erroneous feedback in OpenAI Gym continuous control benchmarks, both for simulated and real human teachers.
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