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A Survey on Policy Search for Robotics
TLDR
This work classifies model-free methods based on their policy evaluation strategy, policy update strategy, and exploration strategy and presents a unified view on existing algorithms. Expand
Probabilistic Movement Primitives
TLDR
This work analytically derive a stochastic feedback controller which reproduces the given trajectory distribution for robot movement control and presents a probabilistic formulation of the MP concept that maintains a distribution over trajectories. Expand
Policy evaluation with temporal differences: a survey and comparison
TLDR
Policy Evaluation with Temporal Differences: A Survey and Comparison and Comparison Journal of Machine Learning Research, 15, 809-883. Expand
Hierarchical Relative Entropy Policy Search
TLDR
This work defines the problem of learning sub-policies in continuous state action spaces as finding a hierarchical policy that is composed of a high-level gating policy to select the low-level sub-Policies for execution by the agent and treats them as latent variables which allows for distribution of the update information between the sub- policies. Expand
An Algorithmic Perspective on Imitation Learning
TLDR
This work provides an introduction to imitation learning, dividing imitation learning into directly replicating desired behavior and learning the hidden objectives of the desired behavior from demonstrations (called inverse optimal control or inverse reinforcement learning [Russell, 1998]). Expand
Data-Efficient Generalization of Robot Skills with Contextual Policy Search
TLDR
This work proposes a new model-based policy search approach that can also learn contextual upper-level policies and achieves a substantial improvement in learning speed compared to existing methods on simulated and real robotic tasks. Expand
Interaction primitives for human-robot cooperation tasks
TLDR
This paper proposes to learn interaction skills by observing how two humans engage in a similar task, and introduces a new representation called Interaction Primitives, which builds on the framework of dynamic motor primitives by maintaining a distribution over the parameters of the DMP. Expand
Using probabilistic movement primitives in robotics
TLDR
A stochastic feedback controller is derived that reproduces the encoded variability of the movement and the coupling of the degrees of freedom of the robot by using a probabilistic representation. Expand
Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks
TLDR
An interaction learning method for collaborative and assistive robots based on movement primitives that allows for both action recognition and human–robot movement coordination and is scalable in relation to the number of tasks. Expand
A Survey of Preference-Based Reinforcement Learning Methods
TLDR
A unified framework for PbRL is provided that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity. Expand
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