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Found 5 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.
GPPF: A General Perception Pre-training Framework via Sparsely Activated Multi-Task Learning
SIMT lays the foundation of pre-training with large-scale multi-task multi-domain datasets and is proved essential for stable training in our GPPF experiments.
Learning Objective Functions Incrementally by Inverse Optimal Control
This paper proposes an inverse optimal control method which enables a robot to incrementally learn a control objective function from a collection of trajectory segments.
Robotics
