Search Results for author: Pascal Klink

Found 16 papers, 4 papers with code

Domain Randomization via Entropy Maximization

no code implementations3 Nov 2023 Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D'Eramo, Georgia Chalvatzaki

Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL).

Reinforcement Learning (RL)

On the Benefit of Optimal Transport for Curriculum Reinforcement Learning

no code implementations25 Sep 2023 Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

In this work, we focus on framing curricula as interpolations between task distributions, which has previously been shown to be a viable approach to CRL.

reinforcement-learning

Function-Space Regularization for Deep Bayesian Classification

no code implementations12 Jul 2023 Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters

Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior.

Adversarial Robustness Classification +3

Self-Paced Absolute Learning Progress as a Regularized Approach to Curriculum Learning

no code implementations9 Jun 2023 Tobias Niehues, Ulla Scheler, Pascal Klink

Curricula based on Absolute Learning Progress (ALP) have proven successful in different environments, but waste computation on repeating already learned behaviour in new tasks.

reinforcement-learning

Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics

no code implementations2 Nov 2022 Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters

We derive two efficient variational inference techniques to learn these representations and highlight the advantages of hierarchical infinite local regression models, such as dealing with non-smooth functions, mitigating catastrophic forgetting, and enabling parameter sharing and fast predictions.

regression Variational Inference

Metrics Matter: A Closer Look on Self-Paced Reinforcement Learning

no code implementations29 Sep 2021 Pascal Klink, Haoyi Yang, Jan Peters, Joni Pajarinen

Experiments demonstrate that the resulting introduction of metric structure into the curriculum allows for a well-behaving non-parametric version of SPRL that leads to stable learning performance across tasks.

reinforcement-learning Reinforcement Learning (RL)

Boosted Curriculum Reinforcement Learning

no code implementations ICLR 2022 Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

This approach, which we refer to as boosted curriculum reinforcement learning (BCRL), has the benefit of naturally increasing the representativeness of the functional space by adding a new residual each time a new task is presented.

reinforcement-learning Reinforcement Learning (RL)

Function-Space Variational Inference for Deep Bayesian Classification

no code implementations29 Sep 2021 Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters

Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior predictive distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior.

Adversarial Robustness Classification +3

Reinforcement Learning using Guided Observability

no code implementations22 Apr 2021 Stephan Weigand, Pascal Klink, Jan Peters, Joni Pajarinen

Due to recent breakthroughs, reinforcement learning (RL) has demonstrated impressive performance in challenging sequential decision-making problems.

Decision Making OpenAI Gym +3

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

1 code implementation25 Feb 2021 Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives.

reinforcement-learning Reinforcement Learning (RL)

A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning

1 code implementation10 Nov 2020 Hany Abdulsamad, Peter Nickl, Pascal Klink, Jan Peters

Probabilistic regression techniques in control and robotics applications have to fulfill different criteria of data-driven adaptability, computational efficiency, scalability to high dimensions, and the capacity to deal with different modalities in the data.

Computational Efficiency

Self-Paced Deep Reinforcement Learning

1 code implementation NeurIPS 2020 Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning.

Open-Ended Question Answering reinforcement-learning +1

Generalized Mean Estimation in Monte-Carlo Tree Search

no code implementations1 Nov 2019 Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen

Finally, we empirically demonstrate the effectiveness of our method in well-known MDP and POMDP benchmarks, showing significant improvement in performance and convergence speed w. r. t.

Self-Paced Contextual Reinforcement Learning

1 code implementation7 Oct 2019 Pascal Klink, Hany Abdulsamad, Boris Belousov, Jan Peters

Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots.

reinforcement-learning Reinforcement Learning (RL)

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