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Found 11 papers, 0 papers with code
Policy Search for Motor Primitives in Robotics
We compare this algorithm to alternative parametrized policy search methods and show that it outperforms previous methods.
Fitted Q-iteration by Advantage Weighted Regression
Recently, fitted Q-iteration (FQI) based methods have become more popular due to their increased sample efficiency, a more stable learning process and the higher quality of the resulting policy.
Local Gaussian Process Regression for Real Time Online Model Learning
Inspired by local learning, we propose a method to speed up standard Gaussian Process regression (GPR) with local GP models (LGP).
Using Bayesian Dynamical Systems for Motion Template Libraries
Motor primitives or motion templates have become an important concept for both modeling human motor control as well as generating robot behaviors using imitation learning.
Switched Latent Force Models for Movement Segmentation
Latent force models encode the interaction between multiple related dynamical systems in the form of a kernel or covariance function.
Movement extraction by detecting dynamics switches and repetitions
Many time-series such as human movement data consist of a sequence of basic actions, e. g., forehands and backhands in tennis.
A Non-Parametric Approach to Dynamic Programming
In this paper, we consider the problem of policy evaluation for continuous-state systems.
Algorithms for Learning Markov Field Policies
We present a new graph-based approach for incorporating domain knowledge in reinforcement learning applications.
Probabilistic Movement Primitives
In order to use such a trajectory distribution for robot movement control, we analytically derive a stochastic feedback controller which reproduces the given trajectory distribution.
Model-Based Relative Entropy Stochastic Search
Stochastic search algorithms are general black-box optimizers.
Catching heuristics are optimal control policies
In this paper, we show that interception strategies appearing to be heuristics can be understood as computational solutions to the optimal control problem faced by a ball-catching agent acting under uncertainty.
