9 citations · 34 across the 14 of their papers we have counts for
9 papers · 1 filter
Pseudo-Likelihood Inference
Theo Gruner, Boris Belousov, Fabio Muratore +2
Simulation-Based Inference (SBI) is a common name for an emerging family of approaches that infer the model parameters when the likelihood is intractable. Existing SBI methods eith…
How Crucial is Transformer in Decision Transformer?
Max Siebenborn, Boris Belousov, Junning Huang +1
Decision Transformer (DT) is a recently proposed architecture for Reinforcement Learning that frames the decision-making process as an auto-regressive sequence modeling problem and…
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…
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints
Michael Lutter, Boris Belousov, Kim Listmann +2
Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learni…
Receding Horizon Curiosity
Matthias Schultheis, Boris Belousov, Hany Abdulsamad +1
Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion. A principled treatme…
Self-Paced Contextual Reinforcement Learning
Pascal Klink, Hany Abdulsamad, Boris Belousov +1
Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots. Although contextual reinforcement learning provides a p…
Entropic Regularization of Markov Decision Processes
Boris Belousov, Jan Peters
An optimal feedback controller for a given Markov decision process (MDP) can in principle be synthesized by value or policy iteration. However, if the system dynamics and the rewar…
Entropic Risk Measure in Policy Search
David Nass, Boris Belousov, Jan Peters
With the increasing pace of automation, modern robotic systems need to act in stochastic, non-stationary, partially observable environments. A range of algorithms for finding param…
f-Divergence constrained policy improvement
Boris Belousov, Jan Peters
To ensure stability of learning, state-of-the-art generalized policy iteration algorithms augment the policy improvement step with a trust region constraint bounding the informatio…