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Found 7 papers, 0 papers with code
Adaptive Policy Learning to Additional Tasks
This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task.
Policy Learning based on Deep Koopman Representation
This paper proposes a policy learning algorithm based on the Koopman operator theory and policy gradient approach, which seeks to approximate an unknown dynamical system and search for optimal policy simultaneously, using the observations gathered through interaction with the environment.
A Data-Driven Approach for Inverse Optimal Control
By assuming the objective function to be learned is parameterized as a linear combination of features with unknown weights, the proposed approach for IOC is able to achieve a Koopman representation of the unknown dynamics and the unknown weights in objective function together.
Deep Koopman Learning of Nonlinear Time-Varying Systems
This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the Koopman operator and deep neural networks.
Deep Learning of Koopman Representation for Control
The OpenAI Gym environment, employed for Reinforcement Learning-based control design, is used for data generation and learning of Koopman operator in control setting.
Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments
Proper path planning is the first step of robust and efficient autonomous navigation for mobile robots.
Robotics Systems and Control Systems and Control
Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach
From the results of the experiments, we compare these two methods in terms of control strategies and the effectiveness under various initialization conditions.
