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Found 5 papers, 3 papers with code
Interactive Imitation Learning in State-Space
Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering.
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach
However, DRL has several limitations when used in real-world problems (e. g., robotics applications).
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.
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs).
