Search Results for author: Carlos Celemin

Found 5 papers, 3 papers with code

Interactive Imitation Learning in State-Space

1 code implementation2 Aug 2020 Snehal Jauhri, Carlos Celemin, Jens Kober

Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering.

Imitation Learning

Deep Reinforcement Learning with Feedback-based Exploration

2 code implementations14 Mar 2019 Jan Scholten, Daan Wout, Carlos Celemin, Jens Kober

We employ binary corrective feedback as a general and intuitive manner to incorporate human intuition and domain knowledge in model-free machine learning.

Continuous Control OpenAI Gym

Learning Gaussian Policies from Corrective Human Feedback

no code implementations12 Mar 2019 Daan Wout, Jan Scholten, Carlos Celemin, Jens Kober

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.

Continuous Control Gaussian Processes +1

Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks

1 code implementation30 Sep 2018 Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober

Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs).

Car Racing Decision Making

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