62 citations · 185 across the 12 of their papers we have counts for
8 papers · 1 filter
Sharing Knowledge in Multi-Task Deep Reinforcement Learning
Carlo D'Eramo, Davide Tateo, Andrea Bonarini +2
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption tha…
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
Pascal Klink, Hany Abdulsamad, Boris Belousov +3
Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives. For reinforcement…
Convex Regularization in Monte-Carlo Tree Search
Tuan Dam, Carlo D'Eramo, Jan Peters +1
Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. The recent AlphaGo and AlphaZero algorithms have shown how to successfully combine…
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts
Riad Akrour, Davide Tateo, Jan Peters
Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. However, these successes are mostly achiev…
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation
Hany Abdulsamad, Jan Peters
The control of nonlinear dynamical systems remains a major challenge for autonomous agents. Current trends in reinforcement learning (RL) focus on complex representations of dynami…
Self-Paced Deep Reinforcement Learning
Pascal Klink, Carlo D'Eramo, Jan Peters +1
Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning. Despite empirical su…
Deep Reinforcement Learning with Weighted Q-Learning
Andrea Cini, Carlo D'Eramo, Jan Peters +1
Reinforcement learning algorithms based on Q-learning are driving Deep Reinforcement Learning (DRL) research towards solving complex problems and achieving super-human performance…
Real Time Trajectory Prediction Using Deep Conditional Generative Models
Sebastian Gomez-Gonzalez, Sergey Prokudin, Bernhard Scholkopf +1
Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot syste…