Arxiv: New paper on "Deep Reinforcement Learning for Swarm Systems" plus videos and code

Arxiv: New paper on “Deep Reinforcement Learning for Swarm Systems” plus videos and code

Abstract: Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to…

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Arxiv: Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning

Arxiv: Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning

Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local…

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IROS 2017: Hybrid control trajectory optimization under uncertainty

IROS 2017: Hybrid control trajectory optimization under uncertainty

Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e….

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IJCAI 2017: Contextual CMA-ES

IJCAI 2017: Contextual CMA-ES

Many stochastic search algorithms are designed to optimize a fixed objective function to learn a task, i.e., if the objective function changes slightly, for example,…

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JMLR 2017: Non-parametric Policy Search with Limited Information Loss.

JMLR 2017: Non-parametric Policy Search with Limited Information Loss.

Learning complex control policies from non-linear and redundant sensory input is an important challenge for reinforcement learning algorithms. Non-parametric methods that approximate values functions or transition…

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IJJR 2017:  Learning Movement Primitive Libraries through Probabilistic Segmentation

IJJR 2017: Learning Movement Primitive Libraries through Probabilistic Segmentation

Movement primitives are a well established approach for encoding and executing movements. While the primitives themselves have been extensively researched, the concept of movement primitive libraries…

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ICML 2017: Local Bayesian Optimization

ICML 2017: Local Bayesian Optimization

Bayesian optimization is renowned for its sample efficiency but its application to higher dimensional tasks is impeded by its focus on global optimization. To scale to higher dimensional problems,…

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AURO2017: Using Probabilistic Movement Primitives in Robotics

AURO2017: Using Probabilistic Movement Primitives in Robotics

Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of…

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GECCO 2017: Deriving and Improving CMA-ES with Information-Geometric Trustregions

GECCO 2017: Deriving and Improving CMA-ES with Information-Geometric Trustregions

CMA-ES is one of the most popular stochastic search algorithms. It performs favourably in many tasks without the need of extensive parameter tuning. The algorithm…

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ICAPS 2017: State-regularized policy search for linearized dynamical systems

ICAPS 2017: State-regularized policy search for linearized dynamical systems

Trajectory-Centric Reinforcement Learning and Trajectory Optimization methods optimize a sequence of feedbackcontrollers by taking advantage of local approximations of model dynamics and cost functions. Stability of the policy…

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